Background

Clinical Genetic Tests

BREVAGenplus

BREVAGenplus evaluates breast cancer-associated single nucleotide variants (SNVs) identified in genome-wide association studies. The first-generation test, BREVAGen, included 7 SNVs. Allman et al (2015) reported the test included over 70 susceptibility SNVs.^1, Risk is calculated by combining individual SNV risks with the Gail model risk. BREVAGenplus has been evaluated for use in African-American, white, and Hispanic patient samples age 35 years and older. BREVAGenplus does not detect known high-risk variants (e.g., in BRCA). According to the BREVAGenplus website, the test is

“not applicable to women who are already at high risk of breast cancer including those that have a personal or extensive family history of breast and/or ovarian cancer, LCIS [lobular carcinoma in situ], DCIS [ductal carcinoma in situ], AH [atypical hyperplasia] or have thoracic RT [radiotherapy] under 30y. Any women with these risk factors are already at increased risk of breast cancer and should be screened and followed as such."^2,

Regulatory Status

Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Amendments. BREVAGenplus® (Phenogen Sciences, a subsidiary of Genetic Technologies) is available under the auspices of the Clinical Laboratory Improvement Amendments. Laboratories that offer laboratory-developed tests must be licensed by the Clinical Laboratory Improvement Amendments for high-complexity testing. To date, the U.S. Food and Drug Administration has chosen not to require any regulatory review of this test.

Related Policies

• Genetic Testing for BRCA1 or BRCA2 for Hereditary Breast/Ovarian Cancer Syndrome and Other High-Risk Cancers (Policy #012 in the Pathology Section)

For Medicare Advantage, please refer to the Medicare Coverage Section below for coverage guidance.)

1. Testing for one or more single nucleotide variants to predict an individual’s risk of breast cancer is considered investigational.

2. The BREVAGenplus™ breast cancer risk tests is considered investigational for all indications, including but not limited to use as a method of estimating individual member risk for developing breast cancer.


Policy Guidelines: (Information to guide medical necessity determination based on the criteria contained within the policy statements above.)

Genetics Nomenclature Update
The Human Genome Variation Society nomenclature is used to report information on variants found in DNA and serves as an international standard in DNA diagnostics. It is being implemented for genetic testing medical policy updates starting in 2017 (see Table PG1). The Society’s nomenclature is recommended by the Human Variome Project, the HUman Genome Organization, and by the Human Genome Variation Society itself.

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology standards and guidelines for interpretation of sequence variants represent expert opinion from both organizations, in addition to the College of American Pathologists. These recommendations primarily apply to genetic tests used in clinical laboratories, including genotyping, single genes, panels, exomes, and genomes. Table PG2 shows the recommended standard terminology“pathogenic,” “likely pathogenic,” “uncertain significance,” “likely benign,” and “benign”to describe variants identified that cause Mendelian disorders.

Table PG1. Nomenclature to Report on Variants Found in DNA

Previous	Updated	Definition
Mutation	Disease-associated variant	Disease-associated change in the DNA sequence
	Variant	Change in the DNA sequence
	Familial variant	Disease-associated variant identified in a proband for use in subsequent targeted genetic testing in first-degree relatives

Table PG2. ACMG-AMP Standards and Guidelines for Variant Classification

Variant Classification	Definition
Pathogenic	Disease-causing change in the DNA sequence
Likely pathogenic	Likely disease-causing change in the DNA sequence
Variant of uncertain significance	Change in DNA sequence with uncertain effects on disease
Likely benign	Likely benign change in the DNA sequence
Benign	Benign change in the DNA sequence

ACMG: American College of Medical Genetics and Genomics; AMP: Association for Molecular Pathology.

Genetic Counseling
Experts recommend formal genetic counseling for patients who are at risk for inherited disorders and who wish to undergo genetic testing. Interpreting the results of genetic tests and understanding risk factors can be difficult for some patients; genetic counseling helps individuals understand the impact of genetic testing, including the possible effects the test results could have on the individual or their family members. It should be noted that genetic counseling may alter the utilization of genetic testing substantially and may reduce inappropriate testing; further, genetic counseling should be performed by an individual with experience and expertise in genetic medicine and genetic testing methods.


Medicare Coverage:
There is no National Coverage Determination (NCD). In the absence of an NCD, coverage decisions are left to the discretion of Local Medicare Carriers. Novitas Solutions, Inc, the Local Medicare Carrier for jurisdiction JL, has not issued a determination for this service. Therefore, Medicare Advantage Products will follow the Horizon BCBSNJ Medical Policy.

PROPRIETARY LABS (Labs that are the sole source for the diagnostic lab test)

For labs which are proprietary (that is, the sole source for the diagnostic lab test involved), Medicare Advantage Products will follow the Medicare Local Coverage Determination of the State where the proprietary lab is located.


[RATIONALE: This policy was created in 2010 and has been updated regularly with searches of the MEDLINE database. The most recent literature update was performed through August 5, 2019.

Evidence reviews assess whether a medical test is clinically useful. A useful test provides information to make a clinical management decision that improves the net health outcome. That is, the balance of benefits and harms is better when the test is used to manage the condition than when another test or no test is used to manage the condition.

The first step in assessing a medical test is to formulate the clinical context and purpose of the test. The test must be technically reliable, clinically valid, and clinically useful for that purpose. Evidence reviews assess the evidence on whether a test is clinically valid and clinically useful. Technical reliability is outside the scope of these reviews, and credible information on technical reliability is available from other sources.

Single Nucleotide Variants (SNVs) and Average Breast Cancer Risk

Clinical Context and Test Purpose

Rare, single-gene variants conferring a high risk of breast cancer have been linked to hereditary breast cancer syndromes. Examples are variants in BRCA1 and BRCA2. These, and a few other genes, account for less than 25% of inherited breast cancer. Moderate risk alleles, such as variants in the CHEK2 gene, are also relatively rare and apparently explain very little of the genetic risk.

In contrast, several common SNVs associated with breast cancer have been identified primarily through genome-wide association studies of very large case-control populations. These alleles occur with high frequency in the general population, and the increased breast cancer risk associated with each is very small relative to the general population risk. Some have suggested that these common-risk SNVs could be combined for individualized risk prediction either alone or in combination with traditional predictors; personalized breast cancer screening programs could then vary by starting age and intensity according to risk. Along these lines, the American Cancer Society has recommended that women at high risk (>20% lifetime risk) should undergo breast magnetic resonance imaging and a mammogram every year, and those at moderately increased risk (15%-20% lifetime risk) should talk with their doctors about the benefits and limitations of adding magnetic resonance imaging screening to their yearly mammogram.^3,

The purpose of genetic testing in asymptomatic individuals is to predict the risk of disease occurrence. The criteria under which prognostic testing may be considered clinically useful are as follows:

• An association of the marker with the disease has been established; and
• The clinical utility of identifying the variants has been established (e.g., by demonstrating that testing will lead to changes in surveillance).

The following PICOs were used to select literature to inform this policy.

Patients

The relevant population of interest is individuals who have not been identified as being at high risk of breast cancer. This population would include individuals who do not have a family member who has had breast cancer.

Interventions

The intervention of interest is testing for common SNVs associated with a small increase in the risk of breast cancer. Patients who are asymptomatic and at average risk of breast cancer by clinical criteria are actively managed by internists in an outpatient clinical setting.

Comparator

The following practice is currently being used to predict the risk of breast cancer: standard clinical risk prediction without testing for common SNVs associated with risk of breast cancer. Patients who are asymptomatic and at average risk of breast cancer by clinical criteria are actively managed by internists in an outpatient clinical setting.

Outcomes

The outcomes of interest are a reclassification of individuals from normal risk and evidence of a change in management (e.g., preventive or screening strategies) that results in improved health outcomes. Follow-up over 5 to 10 years is needed to monitor the occurrence of breast cancer.

Study Selection Criteria

Methodologically credible studies were selected using the following principles:

• To assess efficacy outcomes, comparative controlled prospective trials were sought, with a preference for randomized controlled trials;
• In the absence of such trials, comparative observational studies were sought, with a preference for prospective studies.
• To assess longer term outcomes and adverse events, single-arm studies that capture longer periods of follow-up and/or larger populations were sought.
• Studies with duplicative or overlapping populations were excluded.

Assessment of technical reliability focuses on specific tests and operators and requires review of unpublished and often proprietary information. Review of specific tests, operators, and unpublished data are outside the scope of this policy and alternative sources exist. This policy focuses on the clinical validity and clinical utility.

Clinically Valid

A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).

Genome-wide association studies (GWAS) examine the entire genome of thousands of subjects for SNVs at semi-regular intervals and attempt to associate SNV alleles with particular diseases. Several case-control GWAS, primarily in white women, have investigated common-risk markers of breast cancer. A number of SNVs associated with breast cancer have been reported at a high level of statistical significance and have been validated in 2 or more large, independent studies.^{4,5,6,7,8,9,10,11,12,} SNVs associated with breast cancer risk in Asian and African women have been the subject of more than a dozen articles.^{13,14,15,16,17,18,19,20,21,22,23,24,25,26,}

Systematic Reviews

A number of meta-analyses have investigated the association between breast cancer and individual SNVs. Meta-analyses of case-control studies have indicated that specific SNVs are associated with increased or decreased breast cancer risk (see Table 1). Other meta-analyses have revealed the interaction between the environment (e.g., obesity, age at menarche)^27,28, or ethnicity^{29,30,31,32,33,} and breast cancer risk conferred by certain SNVs. Zhou et al (2013) found that a specific variant in the vitamin D receptor gene increased breast cancer risk in African-American but not white women.^34,Breast cancer risk associated with SNVs in microRNAs is commonly modified by ethnicity.^35,36,37,38, Meta-analyses of GWAS have identified SNVs at new breast cancer susceptibility loci.^39,40,41, All of these markers are considered to be in an investigational phase of development.

Milne et al (2014), on behalf of the Breast Cancer Association Consortium, conducted a mega-analysis of 46,450 case patients and 42,461 controls from 38 international meta-analytic studies.^42, Reviewers assessed 2-way interactions among 3277 breast cancer-associated SNVs. Of 2.5 billion possible 2-SNV combinations, none were statistically significantly associated with breast cancer risk. The meta-analysis suggested that risk models may be simplified by eliminating interaction terms. Reviewers cautioned that despite the large sample size, the study might have been underpowered to detect very small interaction effects, which tend to be smaller than the main effects.

Joshi et al (2014), also on behalf of the Breast and Prostate Cancer Cohort Consortium, conducted a meta-analysis of 8 prospective cohort studies conducted in the United States, Europe, and Australia to examine 2-way interactions between genetic and established clinical risk factors.^43, Based on published GWAS, 23 SNVs were selected for analysis in 10,146 cases of invasive breast cancer and 12,760 controls. Patients were of European ancestry and matched on age and other factors specific to each study. After correction for multiple comparisons, a statistically significant excess in relative risk was attributed to the interaction between rs10483813 variants in the RAD51L1 gene and body mass index (BMI).

Table 1. Examples of Meta-Analyses of SNVs and Associations With Breast Cancer

SNVs	Association			Study
	Positive	None	Protective
2q35 [rs13387042]	⚫			Gu et al (2013)44,
8q24 [G-allele of rs13281615]	⚫			Gong et al (2013)45,
8q24 [homozygous A-alleles of rs13281615]			⚫	Gong et al (2013)45,
AKAP9 [M463I]	⚫			Milne et al (2014)46,
ATR-CHEK1 checkpoint pathway genesa		⚫		Lin et al (2013)47,
ATXN7 [K264R]	⚫			Milne et al (2014)46,
Chemotactic cytokinesb		⚫		Bodelon et al (2013)48,
COMT [V158M]			⚫	He et al (2012)49,
COX2 [rs20417]	⚫			Dai et al (2014)50,
COX2 [rs689466]			⚫	Dai et al (2014)50,
COX2 [rs5275]		⚫		Dai et al (2014)50,
COX11 [rs6504950]			⚫	Tang et al (2012)51,
CYP1A1 [T3801C]	⚫			He et al (2014)52,
CYP1A2 1F [A-allele of rs762551]	⚫			Tian et al (2013)53,
CYP19 [rs10046]		⚫		Pineda et al (2013)54,
Fibroblast growth factor receptor genesc		⚫		kConFab Investigators (2014)55,
IL-10 [rs1800871]		⚫		Yu et al (2013)56,
IRS1 [rs1801278]	⚫			Zhang et al (2013)57,
MAP3K1 [C-allele of rs889312 and G-allele of rs16886165]	⚫			Zheng et al (2014)58,
MDM2 [rs2279744]	⚫			Gao et al (2014)59,
MDR1 [C3435T]	⚫			Wang et al (2013)60,
MTR [A(2756G]	⚫	⚫		Zhong et al (2013)61,
PON1 [L55M]	⚫			Saadat et al (2012)62,
STK15 [F31I]	⚫			Qin et al (2013)63,
STK15 [V571I]		⚫		Qin et al (2013)63,
TCF7L2 [rs7903146]	⚫			Chen et al (2013)64,
VDR [rs731236]	⚫			Perna et al (2013)65,
VDR [rs2228570]	⚫			Zhang et al (2014)66,
VEGF [C936T]		⚫		Li et al (2015)67,
XRCC2 [R188H]		⚫		He et al (2014)68,
XRCC3 [A17893G]			⚫	He et al (2012)69,
XRCC3 [T241M]	⚫			He et al (2012)69,

SNV: single nucleotide variant.

^a Forty ATR and 50 CHEK1 SNVs genotyped.

^b Thirty-four SNVs and groups of SNVs genotyped in 8 chemokine candidate genes: CCL3, CCL4, CCL5, CCL20, CCR5, CCR6, CXCL12, and CXCR4.

^c Three hundred eighty-four SNVs genotyped in FGFR1, FGFR3, FGFR4, and FGFRL1.

Many more genetic risk markers remain to be discovered because substantial unexplained heritability remains.^70, Michailidou et al (2013), researchers from the Collaborative Oncological Gene-Environment Study group, a mega-consortium established to follow-up previous GWAS and candidate gene association studies, identified 41 additional SNVs associated with breast cancer and estimated that “more than 1000 additional loci are involved in breast cancer susceptibility.”^39, One reason more genetic associations have not been found is that even large GWAS are underpowered to detect uncommon genetic variants.^71, As the cost of whole-genome sequencing continues to decrease, some predict that this will become the preferred avenue for researching risk variants.

Reeves et al (2010) evaluated the performance of a panel of 7 SNVs associated with breast cancer in 10,306 women with breast cancer and 10,383 without cancer in the U.K.^72, The risk panel also contained 5 SNVs included in the deCODE Breast Cancer test and used a similar multiplicative approach. Sensitivity studies were performed using 4 SNVs and using 10 SNVs, both demonstrating no significant change in performance. Although the risk score showed marked differences in risk between the upper quintile of patients (8.8% cumulative risk to age 70 years) and the lower quintile of patients (4.4%), these changes were not viewed as clinically useful when compared with patients with an estimated overall background risk of 6.3%. Simple information on patient histories was noted; e.g., the presence of 1 or 2 first-degree relatives with breast cancer provided equivalent or superior risk discrimination (9.1% and 15.4%, respectively).

Pharoahet al (2008) considered a combination of 7 well-validated SNVs associated with breast cancer, 5 of which are included in the deCODE Breast Cancer test.^73, A model that simply multiplies the individual risks of the 7 common SNVs was assumed; such a model would explain approximately 5% of the total genetic risk of nonfamilial breast cancer. Applying the model to the population of women in the U.K., the risk profile provided by the 7 SNVs did 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 suggested that a population screening program could be personalized with results of SNV panel testing. They concluded that no women would be included in the high-risk category (defined as 20% risk within the next 10 years at age 40 to 49 years, according to the National Institute for Health and Care Excellence), and therefore none would warrant the addition of magnetic resonance imaging screening or consideration of more aggressive intervention.

BREVAGen and BREVAGenplus

A study by Allman et al (2015) included 7539 African American and 3363 Hispanic women from the Women’s Health Initiative.^1, Adding a risk score based on over 70 susceptibility loci improved risk prediction by about 10% to 19% over the Gail model and 18% to 26% over the International Breast Cancer Intervention Study risk prediction for African Americans and Hispanics, respectively.

Dite et al (2013) published a similar case-control study of the same 7 SNVs, assuming the same multiplicative model (based on the independent risks of each SNV).^74, The predictive ability of the Gail model with and without the 7 SNV panel was compared in 962 case patients and 463 controls, all 35 years of age or older (mean age, »45 years). The area under the curve (AUC) of the Gail model was 0.58 (95% confidence interval [CI], 0.54 to 0.61); in combination with the 7-SNV panel, AUC increased to 0.61 (95% CI, 0.58 to 0.64; p<0.001). In reclassification analysis, 12% of cases and controls were correctly reclassified, and 9% of cases and controls were incorrectly reclassified when the 7-SNV panel was added to the Gail model. Risk classes were defined by 5-year risk of developing breast cancer (<1.5%, ≥1.5% to <2.0%, and ≥2.0%). Although the addition of the 7-SNV panel to the Gail model improved predictive accuracy, the magnitude of improvement was small, overall accuracy moderate, and impact on health outcomes uncertain.

Mealiffe et al (2010) published a clinical validation study of the BREVAGen test.^75, The authors evaluated a 7-SNV panel in a nested case-control cohort of 1664 case patients and 1636 controls. A model that multiplied the individual risks of the 7 SNVs was assumed, and the resulting genetic risk score was assessed as a potential replacement for or add-on test to the Gail clinical risk model. The net reclassification improvement was used to evaluate performance. Combining 7 validated SNVs with the Gail model resulted in a modest improvement in classification of breast cancer risks, but the AUC only increased from 0.557 to 0.594 (0.50 represents no discrimination, 1.0 perfect discrimination). The impact of reclassification on the net health outcome was not evaluated. The authors suggested that best use of the test might be in patients who would benefit from enhanced or improved risk assessment (e.g., those classified as intermediate risk by the Gail model).

Other Clinical Genetic Tests

Curtit et al (2017) analyzed 8703 patients with early breast cancer who were in prospective case cohorts (SIGNAL and PHARE).^76, The primary aim was to identify associations between a 94-SNV risk score, drawn from previous literature, and invasive disease-free survival. Patients in different quartiles of the 94-SNV risk score were assessed for invasive disease-free survival and overall survival but showed no significant difference between groups (invasive disease-free survival hazard ratio, 0.993; 95% CI, 0.981 to 1.005; p=0.26). Prognostic factors such as age at diagnosis, size of tumor, and metastasis status did not correlate with the risk score, which further did not distinguish between the 3 breast cancer subtypes represented in this analysis (triple-negative, human epidermal growth factor receptor 2-positive, and hormone receptor-positive human epidermal growth factor receptor 2-negative).

Mavaddat et al (2015) reported a multicenter study that assessed risk stratification using 77 breast cancer-associated SNVs in 33,673 breast cancer cases and 33,381 control women of European descent.^77, Polygenic risk scores were developed based on an additive model plus pairwise interactions between SNVs. Women in the highest 1% of the polygenic risk score had a 3-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio, 3.36; 95% CI, 2.95 to 3.83). The lifetime risk of breast cancer was 16.6% for women in the highest quintile of the risk score and 5.2% for women in the lowest quintile. The discriminative accuracy was 0.622 (95% CI, 0.619 to 0.627).

Other large studies have evaluated 8 to 18 common, candidate SNVs in breast cancer cases and normal controls to determine whether breast cancer assessments based on clinical factors plus various SNV combinations were more accurate than risk assessments based on clinical factors alone.

• Armstrong et al (2013) examined the impact of pretest breast cancer risk prediction on the classification of women with an abnormal mammogram above or below the risk threshold for biopsy.^78, Currently, 1-year probability of breast cancer among women with Breast Imaging-Reporting and Data System (BIRADS) category 3 mammograms is 2%; these women undergo 6-month follow-up rather than biopsy. In contrast, women with BIRADS category 4 mammograms have a 6% (BIRADS category 4A) or greater (BIRADS categories 4B and 4C) probability of developing breast cancer in 1 year; these women are referred for biopsy. Using the Gail model plus 12 SNVs for risk prediction and a 2% biopsy risk threshold, 8% of women with BIRADS category 3 mammograms were reclassified above the threshold for biopsy, and 7% of women with BIRADS category 4A mammograms were reclassified below the threshold. The greatest impact on reclassification was attributed to standard breast cancer risk factors. The net health outcome was not compared between women who were reclassified and those who were not.
• Darabi et al (2012) investigated the performance of 18 breast cancer risk SNVs, together with mammographic percentage density, BMI, and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well-characterized case-control study of postmenopausal Swedish women.^79, Performance of a risk prediction model based on an initial set of 7 breast cancer risk SNVs was improved by including 11 more recently established breast cancer risk SNVs (p<0.001). Adding mammographic percentage density, BMI and all 18 SNVs to a modified Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into 5-year low-, intermediate-, and high-risk categories (p<0.001). It was estimated that using an individualized screening strategy based on risk models incorporating clinical risk factors, mammographic density, and SNVs, would capture 10% more cases. Impacts on the net health outcome from such a change are unknown.
• Campa et al (2011) found no evidence that the 17 SNV breast cancer susceptibility loci modified the associations between established risk factors and breast cancer.^80,
• Zheng et al (2010) found that 8 SNVs, combined with other clinical predictors, were significantly associated with breast cancer risk; the full model gave an AUC of 0.63.^81,
• Wacholder et al (2010) evaluated the performance of a panel of 10 SNVs associated with breast cancer that had, at the time of the study, been validated in at least 3 published GWAS.^82, Cases (n=5590) and controls (n=5998) 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 SNV panel was examined as a risk predictor alone and in addition to readily available components of the Gail model (e.g., diagnosis of atypical hyperplasia was not included). The authors found that adding the SNV panel to the Gail model resulted in slightly better stratification of a woman’s risk than either the SNV panel or the Gail model alone but that this stratification was inadequate to inform clinical practice. For example, only 34% of the women who had breast cancer were assigned to the top 20% risk group. AUC for the combined SNV and Gail model was 62% (50% is random, 100% is perfect).

Section Summary: Clinically Valid

Common SNVs have been shown in meta-analyses and primary studies to be significantly associated with breast cancer risk; some SNVs convey slightly elevated risk compared with the general population risk. Estimates of breast cancer risk, based on SNVs derived from large GWAS and/or from SNVs in other genes known to be associated with breast cancer, are available as a laboratory-developed test service. The literature on these associations is growing, although information about the risk models is proprietary. Available data would suggest that BREVAGenplus may add predictive accuracy to the Gail model. However, the degree of improved risk prediction may be modest, and clinical implications are unclear. Other panel tests have fewer data to support conclusions about their clinical validity. Independent determination of clinical validity in an intended-use population has not been performed. Use of such risk panels for individual patient care or population screening programs is premature because (1) performance of these panels in the intended-use populations is uncertain, and (2) most genetic breast cancer risk has yet to be explained by undiscovered gene variants and SNVs.

Clinically Useful

A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.

Direct Evidence

Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. Because these are intervention studies, the preferred evidence would be from randomized controlled trials.

No randomized controlled trials evaluating the clinical utility of SNV panel testing to predict the risk of breast cancer were identified.

Chain of Evidence

Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.

One potential use of SNV testing is to evaluate the risk of breast cancer for chemoprevention. Cuzick et al (2017) assessed whether a panel of 88 SNVs could improve risk prediction over traditional risk stratification using data from 2 randomized tamoxifen prevention trials.^83, The study included 359 cases and 636 controls, with the 88 SNVs assessed on an Illumina OncoArray that evaluated approximately half a million SNVs. The primary outcome was breast cancer or ductal carcinoma in situ. The 88 SNV score improved discriminability above the Tyrer-Cuzick risk evaluator; however, there was a modest improvement in the percentage of women who were classified as high risk. The percentage of women with a 10-year risk of recurrence of 8% or more was estimated to be 18% for Tyrer-Cuzick and 21% when the 88 SNV score was added. The SNV score did not predict which women would benefit from tamoxifen.

McCarthy et al (2015) examined the impact of BMI, Gail model risk, and a 12-SNV version of the deCODE Breast Cancer test on breast cancer risk prediction and biopsy decisions among women with BI-RADS category 4 mammograms who had been referred for biopsy (N=464).^84, The original deCODE Breast Cancer panel included 7 SNVs; neither panel is currently commercially available. The mean patient age was 49 years, 60% were white, and 31% were black. In multivariate regression models that included age, BMI, Gail risk factors, and SNV panel risk as a continuous variable, a statistically significant association between SNV panel risk and breast cancer diagnosis was observed (odds ratio, 2.30; 95% CI, 1.06 to 4.99; p=0.035). However, categorized SNV panel risks (e.g., relative increase or decrease in risk compared with the general population), resembling how the test would be used in clinical practice, were not statistically associated with breast cancer diagnosis. In subgroups defined by black or white race, SNV panel risk also was not statistically associated with breast cancer diagnosis. Risk estimated by a model that included age, Gail risk factors, BMI, and the SNV panel, reclassified 9 (3.4%) women below a 2% risk threshold for biopsy, none of whom were diagnosed with cancer.

Bloss et al (2011) reported on the psychological, behavioral, and clinical effects of risk scanning in 3639 patients followed for a short time (mean, 5.6 months).^85, These investigators evaluated anxiety, intake of dietary fat, and exercise based on information from genomic testing. There were no significant changes before and after testing and no increase in the number of screening tests obtained in enrolled patients. Although more than half of patients participating in the study indicated an intent to undergo screening in the future, during the study itself, no actual increase was observed.

Section Summary: Clinically Useful

The number of common low-penetrance SNVs associated with breast cancer is rapidly increasing. No studies were identified that provide direct evidence that use of SNV-based risk assessment has any impact on health care outcomes. Indirect evidence from an improvement in risk prediction with an 88 SNV panel has been reported, although the improvement in risk prediction is modest. For the specific loci evaluated by the most recent BREVAGenplus test, there is insufficient evidence to determine whether using breast cancer risk estimates in asymptomatic individuals changes management decisions and improves patient outcomes.

Summary of Evidence

For individuals who are asymptomatic and at average risk of breast cancer by clinical criteria who receive testing for common SNVs associated with a small increase in the risk of breast cancer, the evidence includes observational studies. Relevant outcomes are test validity, morbid events, and quality of life. Clinical genetic tests may improve the predictive accuracy of current clinical risk predictors. However, the magnitude of improvement is small, and clinical significance is uncertain. Whether the potential harms of these tests due to false-negative and false-positive results are outweighed by the potential benefit associated with improved risk assessment is unknown. Evaluation of this technology is further complicated by the rapidly increasing numbers of SNVs associated with a small risk of breast cancer. Long-term prospective studies with large sample sizes are needed to determine the clinical validity and utility of SNV-based models for predicting breast cancer risk. The discriminatory ability offered by the genetic factors currently known is insufficient to inform clinical practice. The evidence is insufficient to determine the effects of the technology on health outcomes.

SUPPLEMENTAL INFORMATION

Practice Guidelines and Position Statements

National Comprehensive Cancer Network

In its guidelines on genetic or familial high-risk assessment of breast and ovarian cancers ( v.3.2019), the National Comprehensive Cancer Network notes the potential for multigene testing to identify intermediate penetrance (moderate risk) genes, but adds that “For many of these genes, there are limited data on the degree of cancer risk and there are no clear guidelines on risk management for carriers of pathogenic/likely pathogenic variants. Not all genes included on available multi-gene tests are necessarily clinically actionable.”^86,In the absence of evidence, guiding follow-up to testing, including risk management strategies, National Comprehensive Cancer Network recommends "that multi-gene testing is ideally offered in the context of professional genetic expertise, for pre- and post-test counseling.”^86,

American Society of Clinical Oncology

For breast cancer risk assessment, the American Society of Clinical Oncology (2013) recommended the Gail model^87, or risk models for women with elevated risk based on family history (e.g., Claus et al [1994]^88, or Tyrer et al [2004]^89,).^90,

U.S. Preventive Services Task Force Recommendations

No U.S. Preventive Services Task Force recommendations for single nucleotide variant testing either in conjunction with or without consideration of clinical factors to predict breast cancer risk have been identified.

Ongoing and Unpublished Clinical Trials

Some currently unpublished trials that might influence this policy are listed in Table 2.

Table 2. Summary of Key Trials

NCT No.	Trial Name	Planned Enrollment	Completion Date
Ongoing
NCT02620852	Enabling a Paradigm Shift: A Preference-Tolerant RCT of Personalized vs. Annual Screening for Breast Cancer (WISDOM)	100,000	Dec 2020

NCT: national clinical trial.]
________________________________________________________________________________________

Horizon BCBSNJ Medical Policy Development Process:

This Horizon BCBSNJ Medical Policy (the “Medical Policy”) has been developed by Horizon BCBSNJ’s Medical Policy Committee (the “Committee”) consistent with generally accepted standards of medical practice, and reflects Horizon BCBSNJ’s view of the subject health care services, supplies or procedures, and in what circumstances they are deemed to be medically necessary or experimental/ investigational in nature. This Medical Policy also considers whether and to what degree the subject health care services, supplies or procedures are clinically appropriate, in terms of type, frequency, extent, site and duration and if they are considered effective for the illnesses, injuries or diseases discussed. Where relevant, this Medical Policy considers whether the subject health care services, supplies or procedures are being requested primarily for the convenience of the covered person or the health care provider. It may also consider whether the services, supplies or procedures are more costly than an alternative service or sequence of services, supplies or procedures that are at least as likely to produce equivalent therapeutic or diagnostic results as to the diagnosis or treatment of the relevant illness, injury or disease. In reaching its conclusion regarding what it considers to be the generally accepted standards of medical practice, the Committee reviews and considers the following: all credible scientific evidence published in peer-reviewed medical literature generally recognized by the relevant medical community, physician and health care provider specialty society recommendations, the views of physicians and health care providers practicing in relevant clinical areas (including, but not limited to, the prevailing opinion within the appropriate specialty) and any other relevant factor as determined by applicable State and Federal laws and regulations.

___________________________________________________________________________________________________________________________

Index:
Use of Common Genetic Variants to Predict Risk of Nonfamilial Breast Cancer
deCODE BreastCancer test
deCODEme Cancer Scan
Common Genetic Variants to Predict Risk of Nonfamilial Breast Cancer
23andme
Navigenics
Non-BRCA Breast Cancer Risk Assessment
Breast Cancer Risk Assessment, Non-BRCA
OncoVue Breast Cancer Risk Test
BREVAGen Beasr Cancer Risk Test
BREVACGenplus

References:
1. Allman R, Dite GS, Hopper JL, et al. SNPs and breast cancer risk prediction for African American and Hispanic women. Breast Cancer Res Treat. Dec 2015;154(3):583-589. PMID 26589314.

2. Genetic Technologies. BREVAGenplus. 2018; http://www.brevagenplus.com/. Accessed September 25, 2018.

3. American Cancer Society. Breast cancer: early detection, diagnosis, and staging topics - Can breast cancer be found early? 2017; https://www.cancer.org/cancer/breast-cancer/screening-tests-and-early-detection.html. Accessed September 25, 2018.

4. Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. Jul 2007;39(7):865-869. PMID 17529974.

5. Easton DF, Pooley KA, Dunning AM, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. Jun 28 2007;447(7148):1087-1093. PMID 17529967.

6. Hunter DJ, Kraft P, Jacobs KB, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. Jul 2007;39(7):870-874. PMID 17529973.

7. Thomas G, Jacobs KB, Kraft P, et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet. May 2009;41(5):579-584. PMID 19330030.

8. Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. Jun 2008;40(6):703-706. PMID 18438407.

9. Gold B, Kirchhoff T, Stefanov S, et al. Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A. Mar 18 2008;105(11):4340-4345. PMID 18326623.

10. Ahmed S, Thomas G, Ghoussaini M, et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet. May 2009;41(5):585-590. PMID 19330027.

11. Zheng W, Long J, Gao YT, et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet. Mar 2009;41(3):324-328. PMID 19219042.

12. Garcia-Closas M, Hall P, Nevanlinna H, et al. Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics. PLoS Genet. Apr 2008;4(4):e1000054. PMID 18437204.

13. Beeghly-Fadiel A, Shu XO, Lu W, et al. Genetic variation in VEGF family genes and breast cancer risk: a report from the Shanghai Breast Cancer Genetics Study. Cancer Epidemiol Biomarkers Prev. Jan 2011;20(1):33-41. PMID 21119072.

14. Cai Q, Wen W, Qu S, et al. Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalize its importance in women of Chinese, Japanese, and European ancestry. Cancer Res. Feb 15 2011;71(4):1344-1355. PMID 21303983.

15. Han W, Woo JH, Yu JH, et al. Common genetic variants associated with breast cancer in Korean women and differential susceptibility according to intrinsic subtype. Cancer Epidemiol Biomarkers Prev. May 2011;20(5):793- 798. PMID 21415360.

16. Jiang Y, Han J, Liu J, et al. Risk of genome-wide association study newly identified genetic variants for breast cancer in Chinese women of Heilongjiang Province. Breast Cancer Res Treat. Jul 2011;128(1):251-257. PMID 21197568.

17. Mong FY, Kuo YL, Liu CW, et al. Association of gene polymorphisms in prolactin and its receptor with breast cancer risk in Taiwanese women. Mol Biol Rep. Oct 2011;38(7):4629-4636. PMID 21125332.

18. Mukherjee N, Bhattacharya N, Sinha S, et al. Association of APC and MCC polymorphisms with increased breast cancer risk in an Indian population. Int J Biol Markers. Jan-Mar 2011;26(1):43-49. PMID 21279955.

19. Ota I, Sakurai A, Toyoda Y, et al. Association between breast cancer risk and the wild-type allele of human ABC transporter ABCC11. Anticancer Res. Dec 2010;30(12):5189-5194. PMID 21187511.

20. Ren J, Wu X, He W, et al. Lysyl oxidase 473 G>A polymorphism and breast cancer susceptibility in Chinese Han population. DNA Cell Biol. Feb 2011;30(2):111-116. PMID 20929399.

21. Yu JC, Hsiung CN, Hsu HM, et al. Genetic variation in the genome-wide predicted estrogen response element- related sequences is associated with breast cancer development. Breast Cancer Res. Jan 31 2011;13(1):R13. PMID 21281495.

22. Ma H, Li H, Jin G, et al. Genetic variants at 14q24.1 and breast cancer susceptibility: a fine-mapping study in Chinese women. DNA Cell Biol. Jun 2012;31(6):1114-1120. PMID 22313133.

23. Dai J, Hu Z, Jiang Y, et al. Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women. Breast Cancer Res. Jan 23 2012;14(1):R17. PMID 22269215.

24. Long J, Cai Q, Sung H, et al. Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet. Feb 2012;8(2):e1002532. PMID 22383897.

25. Huo D, Zheng Y, Ogundiran TO, et al. Evaluation of 19 susceptibility loci of breast cancer in women of African ancestry. Carcinogenesis. Apr 2012;33(4):835-840. PMID 22357627.

26. McCarthy AM, Armstrong K, Handorf E, et al. Incremental impact of breast cancer SNP panel on risk classification in a screening population of white and African American women. Breast Cancer Res Treat. Apr 2013;138(3):889-898. PMID 23474973.

27. Schoeps A, Rudolph A, Seibold P, et al. Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. Genet Epidemiol. Jan 2014;38(1):84-93. PMID 24248812.

28. Nickels S, Truong T, Hein R, et al. Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genet. Apr 2013;9(3):e1003284. PMID 23544014.

29. Pei J, Li F, Wang B. Single nucleotide polymorphism 6q25.1 rs2046210 and increased risk of breast cancer. Tumour Biol. Dec 2013;34(6):4073-4079. PMID 23888322.

30. Wu X, Xu QQ, Guo L, et al. Quantitative assessment of the association between rs2046210 at 6q25.1 and breast cancer risk. PLoS One. Jun 2013;8(6):e65206. PMID 23785413.

31. Liu JJ, Liu JL, Zhang X, et al. A meta-analysis of the association of glutathione S-transferase P1 gene polymorphism with the susceptibility of breast cancer. Mol Biol Rep. Apr 2013;40(4):3203-3212. PMID 23334471.

32. Zheng W, Zhang B, Cai Q, et al. Common genetic determinants of breast-cancer risk in East Asian women: a collaborative study of 23 637 breast cancer cases and 25 579 controls. Hum Mol Genet. Jun 15 2013;22(12):2539-2550. PMID 23535825.

33. Yao S, Graham K, Shen J, et al. Genetic variants in microRNAs and breast cancer risk in African American and European American women. Breast Cancer Res Treat. Oct 2013;141(3):447-459. PMID 24062209.

34. Zhou ZC, Wang J, Cai ZH, et al. Association between vitamin D receptor gene Cdx2 polymorphism and breast cancer susceptibility. Tumour Biol. Dec 2013;34(6):3437-3441. PMID 23821301.

35. Chen QH, Wang QB, Zhang B. Ethnicity modifies the association between functional microRNA polymorphisms and breast cancer risk: a HuGE meta-analysis. Tumour Biol. Jan 2014;35(1):529-543. PMID 23982873.

36. Xu Q, He CY, Liu JW, et al. Pre-miR-27a rs895819A/G polymorphisms in cancer: a meta-analysis. PLoS One. Jun 2013;8(6):e65208. PMID 23762318.

37. Zhong S, Chen Z, Xu J, et al. Pre-mir-27a rs895819 polymorphism and cancer risk: a meta-analysis. Mol Biol Rep. Apr 2013;40(4):3181-3186. PMID 23266669.

38. Fan C, Chen C, Wu D. The association between common genetic variant of microRNA-499 and cancer susceptibility: a meta-analysis. Mol Biol Rep. Apr 2013;40(4):3389-3394. PMID 23271127.

39. Michailidou K, Hall P, Gonzalez-Neira A, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. Apr 2013;45(4):353-361. PMID 23535729.

40. Siddiq A, Couch FJ, Chen GK, et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet. Dec 15 2012;21(24):5373-5384. PMID 22976474.

41. Garcia-Closas M, Couch FJ, Lindstrom S, et al. Genome-wide association studies identify four ER negative- specific breast cancer risk loci. Nat Genet. Apr 2013;45(4):392-398. PMID 23535733.

42. Milne RL, Herranz J, Michailidou K, et al. A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46 450 cases and 42 461 controls from the Breast Cancer Association Consortium. Hum Mol Genet. Apr 1 2014;23(7):1934-1946. PMID 24242184.

43. Joshi AD, Lindstrom S, Husing A, et al. Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium. Am J Epidemiol. Nov 15 2014;180(10):1018-1027. PMID 25255808.

44. Gu C, Zhou L, Yu J. Quantitative assessment of 2q35-rs13387042 polymorphism and hormone receptor status with breast cancer risk. PLoS One. Jul 2013;8(7):e66979. PMID 23894282.

45. Gong WF, Zhong JH, Xiang BD, et al. Single nucleotide polymorphism 8q24 rs13281615 and risk of breast cancer: meta-analysis of more than 100,000 cases. PLoS One. Apr 2013;8(4):e60108. PMID 23565189.

46. Milne RL, Burwinkel B, Michailidou K, et al. Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium. Hum Mol Genet. Nov 15 2014;23(22):6096-6111. PMID 24943594.

47. Lin WY, Brock IW, Connley D, et al. Associations of ATR and CHEK1 single nucleotide polymorphisms with breast cancer. PLoS One. Jul 2013;8(7):e68578. PMID 23844225.

48. Bodelon C, Malone KE, Johnson LG, et al. Common sequence variants in chemokine-related genes and risk of breast cancer in post-menopausal women. Int J Mol Epidemiol Genet. Nov 2013;4(4):218-227. PMID 24319537.

49. He XF, Wei W, Li SX, et al. Association between the COMT Val158Met polymorphism and breast cancer risk: a meta-analysis of 30,199 cases and 38,922 controls. Mol Biol Rep. Jun 2012;39(6):6811-6823. PMID 22297695.

50. Dai ZJ, Shao YP, Ma XB, et al. Association of the three common SNPs of cyclooxygenase-2 gene (rs20417, rs689466, and rs5275) with the susceptibility of breast cancer: an updated meta-analysis involving 34,590 subjects. Dis Markers. Sep 2014;2014:484729. PMID 25214704.

51. Tang L, Xu J, Wei F, et al. Association of STXBP4/COX11 rs6504950 (G>A) polymorphism with breast cancer risk: evidence from 17,960 cases and 22,713 controls. Arch Med Res. Jul 2012;43(5):383-388. PMID 22863968.

52. He XF, Wei W, Liu ZZ, et al. Association between the CYP1A1 T3801C polymorphism and risk of cancer: evidence from 268 case-control studies. Gene. Oct 24 2014;534(2):324-344. PMID 24513335.

53. Tian Z, Li YL, Zhao L, et al. Role of CYP1A2 1F polymorphism in cancer risk: evidence from a meta-analysis of 46 case-control studies. Gene. Jul 25 2013;524(2):168-174. PMID 23628800.

54. Pineda B, Garcia-Perez MA, Cano A, et al. Associations between aromatase CYP19 rs10046 polymorphism and breast cancer risk: from a case-control to a meta-analysis of 20,098 subjects. PLoS One. Jan 2013;8(1):e53902. PMID 23342035.

55. Agarwal D, Pineda S, Michailidou K, et al. FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium. Br J Cancer. Feb 18 2014;110(4):1088-1100. PMID 24548884.

56. Yu Z, Liu Q, Huang C, et al. The interleukin 10 -819C/T polymorphism and cancer risk: a HuGE review and meta-analysis of 73 studies including 15,942 cases and 22,336 controls. OMICS. Apr 2013;17(4):200-214. PMID 23574339.

57. Zhang H, Wang A, Ma H, et al. Association between insulin receptor substrate 1 Gly972Arg polymorphism and cancer risk. Tumour Biol. Oct 2013;34(5):2929-2936. PMID 23708959.

58. Zheng Q, Ye J, Wu H, et al. Association between mitogen-activated protein kinase kinase kinase 1 polymorphisms and breast cancer susceptibility: a meta-analysis of 20 case-control studies. PLoS One. Mar 2014;9(3):e90771. PMID 24595411.

59. Gao J, Kang AJ, Lin S, et al. Association between MDM2 rs 2279744 polymorphism and breast cancer susceptibility: a meta-analysis based on 9,788 cases and 11,195 controls. Ther Clin Risk Manag. May 2014;10:269-277. PMID 24790452.

60. Wang Z, Wang T, Bian J. Association between MDR1 C3435T polymorphism and risk of breast cancer. Gene. Dec 10 2013;532(1):94-99. PMID 24070710.

61. Zhong S, Xu J, Li W, et al. Methionine synthase A2756G polymorphism and breast cancer risk: an up-to-date meta-analysis. Gene. Sep 25 2013;527(2):510-515. PMID 23845785.

62. Saadat M. Paraoxonase 1 genetic polymorphisms and susceptibility to breast cancer: a meta-analysis. Cancer Epidemiol. Apr 2012;36(2):e101-103. PMID 22133529.

63. Qin K, Wu C, Wu X. Two nonsynonymous polymorphisms (F31I and V57I) of the STK15 gene and breast cancer risk: a meta-analysis based on 5966 cases and 7609 controls. J Int Med Res. Aug 2013;41(4):956-963. PMID 23803310.

64. Chen J, Yuan T, Liu M, et al. Association between TCF7L2 gene polymorphism and cancer risk: a meta- analysis. PLoS One. Aug 2013;8(8):e71730. PMID 23951231.

65. Perna L, Butterbach K, Haug U, et al. Vitamin D receptor genotype rs731236 (Taq1) and breast cancer prognosis. Cancer Epidemiol Biomarkers Prev. Mar 2013;22(3):437-442. PMID 23300018.

66. Zhang K, Song L. Association between vitamin D receptor gene polymorphisms and breast cancer risk: a meta- analysis of 39 studies. PLoS One. Apr 2014;9(4):e96125. PMID 24769568.

67. Li J, Ju Y. Association between the functional polymorphism of vascular endothelial growth factor gene and breast cancer: a meta-analysis. Iran J Med Sci. Jan 2015;40(1):2-12. PMID 25649829.

68. He Y, Zhang Y, Jin C, et al. Impact of XRCC2 Arg188His polymorphism on cancer susceptibility: a meta- analysis. PLoS One. Mar 2014;9(3):e91202. PMID 24621646.

69. He XF, Wei W, Su J, et al. Association between the XRCC3 polymorphisms and breast cancer risk: meta- analysis based on case-control studies. Mol Biol Rep. May 2012;39(5):5125-5134. PMID 22161248.

70. Sakoda LC, Jorgenson E, Witte JS. Turning of COGS moves forward findings for hormonally mediated cancers. Nat Genet. Apr 2013;45(4):345-348. PMID 23535722.

71. Hunter DJ, Altshuler D, Rader DJ. From Darwin's finches to canaries in the coal mine--mining the genome for new biology. N Engl J Med. Jun 26 2008;358(26):2760-2763. PMID 18579810.

72. Reeves GK, Travis RC, Green J, et al. Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. JAMA. Jul 28 2010;304(4):426-434. PMID 20664043.

73. Pharoah PD, Antoniou AC, Easton DF, et al. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. Jun 26 2008;358(26):2796-2803. PMID 18579814.

74. Dite GS, Mahmoodi M, Bickerstaffe A, et al. Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. Jun 2013;139(3):887-896. PMID 23774992.

75. Mealiffe ME, Stokowski RP, Rhees BK, et al. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. Nov 3 2010;102(21):1618-1627. PMID 20956782.

76. Curtit E, Pivot X, Henriques J, et al. Assessment of the prognostic role of a 94-single nucleotide polymorphisms risk score in early breast cancer in the SIGNAL/PHARE prospective cohort: no correlation with clinico- pathological characteristics and outcomes. Breast Cancer Res. Aug 22 2017;19(1):98. PMID 28830573.

77. Mavaddat N, Pharoah PD, Michailidou K, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. May 2015;107(5):djv036. PMID 25855707.

78. Armstrong K, Handorf EA, Chen J, et al. Breast cancer risk prediction and mammography biopsy decisions: a model-based study. Am J Prev Med. Jan 2013;44(1):15-22. PMID 23253645.

79. Darabi H, Czene K, Zhao W, et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res. Feb 7 2012;14(1):R25. PMID 22314178.

80. Campa D, Kaaks R, Le Marchand L, et al. Interactions between genetic variants and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium. J Natl Cancer Inst. Aug 17 2011;103(16):1252-1263. PMID 21791674.

81. Zheng W, Wen W, Gao YT, et al. Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women. J Natl Cancer Inst. Jul 7 2010;102(13):972-981. PMID 20484103.

82. Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med. Mar 18 2010;362(11):986-993. PMID 20237344.

83. Cuzick J, Brentnall AR, Segal C, et al. 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. Mar 2017;35(7):743-750. PMID 28029312.

84. McCarthy A, Keller B, Kontos D, et al. The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms. Breast Cancer Res. Jan 8 2015;17(1):1. PMID 25567532.

85. Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. Feb 10 2011;364(6):524-534. PMID 21226570.

86. National Comprehensive Cancer Network (NCCN). NCCN clinical practice guidelines in oncology: genetic/familial high-risk assessment: breast and ovarian. Version 3.2019. https://www.nccn.org/professionals/physician_gls/pdf/genetics_screening.pdf. Accessed August 21, 2019.

87. National Cancer Institute. The Breast Cancer Risk Assessment Tool. n.d.; https://bcrisktool.cancer.gov/. Accessed September 27, 2018.

88. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer. Feb 1 1994;73(3):643-651. PMID 8299086.

89. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. Apr 15 2004;23(7):1111-1130. PMID 15057881.

90. Visvanathan K, Hurley P, Bantug E, et al. Use of pharmacologic interventions for breast cancer risk reduction: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol. Aug 10 2013;31(23):2942-2962. PMID 23835710.

Codes:
(The list of codes is not intended to be all-inclusive and is included below for informational purposes only. Inclusion or exclusion of a procedure, diagnosis, drug or device code(s) does not constitute or imply authorization, certification, approval, offer of coverage or guarantee of payment.)

CPT*

81599