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Use of Common Genetic Variants (Single Nucleotide Polymorphisms) to Predict Risk of Non-familial Breast Cancer

Number 12.04.63

Effective Date June 17, 2015

Revision Date(s) 06/09/15; 06/09/14; 07/08/13, 07/10/12

Replaces 2.04.63

Policy

Testing for one or more single nucleotide polymorphisms (SNPs) to predict an individual’s risk of breast cancer is considered investigational.

The OncoVue® and BREVAGenplus® breast cancer risk tests are considered investigational for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer.

Related Policies

12.04.93

Genetic Cancer Susceptibility Panels Using Next Generation Sequencing

12.04.504

Genetic Testing for Hereditary Breast and/or Ovarian Cancer Syndrome (BRCA1/BRCA2)

Policy Guidelines

BRCA genetic testing should be used in those from high-risk families. (See Related Policies).

SNP Panel Tests

There is no specific CPT code for this test. Effective in 2013, the unlisted multianalyte assay with algorithmic analysis code 81599 would be the most appropriate code to report for this testing when results are reported as a risk score or probability.

Clinical Genetic Tests

OncoVue® (Intergenetics) and BREVAGenplus® (Phenogen Sciences) breast cancer tests are not offered directly to consumers, a physician must order these tests. The Phenogen Sciences website provides a list of BREVAGenplus® participating providers. “Some of the listed physicians may be paid consultants for Phenogen Sciences.” (1)

NOTE: OncoVue® appears to be no longer commercially available.

NOTE: The non-specific code for the OncoVue® or BREVAGenplus® test that might be reported for these tests is below.

Coding

CPT

81599

Unlisted multianalyte assay with algorithmic analysis

Description

Several single nucleotide polymorphisms (SNPs), which are single base-pair variations in the DNA sequence of the genome, have been found to be associated with breast cancer and are common in the population but confer only small increases in risk. Commercially available assays test for several SNPs to predict an individual’s risk of breast cancer relative to the general population. Some of these incorporate clinical information into risk prediction algorithms. The intent of both types of test is to identify subjects at increased risk who may benefit from more intensive surveillance.

Background

Rare, single gene variants conferring a high risk of breast cancer have been linked to hereditary breast cancer syndromes. Examples are mutations in BRCA1 and BRCA2. These, and a few others, 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 SNPs associated with breast cancer have been identified primarily through genome-wide association studies (GWAS) of very large case-control populations. These alleles 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 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 recommends that women at high risk (>20% lifetime risk) should undergo breast magnetic resonance imaging (MRI) 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 MRI screening to their yearly mammogram.(3)

SNP Panel Tests

Several companies, such as those listed in Table 1, offer testing for breast cancer risk profiles using SNPs. Non-U.S. Companies offer testing direct-to-consumers (DTCs). Algorithms or risk models for these tests are proprietary. When reported on company websites, panels range in number from 6 to 15 SNPs.

Table 1. Tests for Breast Cancer Susceptibility Using SNP-Based Risk Panelsa

Company

Location

Test Offered DTC

Number of SNPs Used in Risk Panel

City of Hope Breast Cancer Susceptibility Assay

Duarte, CA

No

7

easyDNA

Elk Grove, CA

Nob

ND

GenePlanet

Dublin, Ireland

Yes

15

DTC: direct-to-consumer; ND: not described; SNP: single nucleotide polymorphism.

a This is not an exhaustive list.

b The easyDNA website includes a “note for U.S. residents” that states “easyDNA would like to inform all its clients that as per the U.S. Food and Drug Administration’s [FDA] directive, it can only provide genetic health testing to U.S. residents if their physician has agreed to the test.” (4)

Clinical Genetic Tests

Two companies currently offer risk assessment based on SNP panel testing and clinical information. Neither is provided as a DTC test. Only BREVAGen® is currently listed in the Genetic Testing Registry of the National Center for Biotechnology Information.

OncoVue®

The OncoVue® Breast Cancer Risk Test (InterGenetics, Oklahoma City, OK) is a proprietary test that evaluates multiple, low-risk SNPs associated with breast cancer. Results are combined with personal history measures to determine breast cancer risk at different times during adulthood. The test does not detect known high-risk genetic factors such as BRCA mutations associated with hereditary breast and ovarian cancer, (see Related Policies). OncoVue® synthesizes various genetic and medical history risk measures into a personalized single-risk estimate for premenopause, perimenopause, and postmenopause for each patient, with comparison to the average population risk at each of these life stages.

For women without a strong family history of breast cancer and at average risk before testing, OncoVue® purports to estimate a woman’s individual risk and place her in standard-, moderate-, or high-risk groups. Results are intended to help a woman and her physician decide if more frequent exams and/or more sophisticated surveillance techniques are indicated.

BREVAGenplus®

BREVAGenplus® (Phenogen Sciences, Charlotte, NC) evaluates breast cancer-associated SNPs identified in GWAS. The first-generation test, BREVAGen, included 7 SNPs. Per the company website, BREVAGenplus incorporates an “expanded panel” of SNPs. Risk is calculated by combining individual SNP 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. Like OncoVue®, BREVAGenplus® does not detect known high-risk mutations, e.g., in BRCA. According to the BREVAGenplus® website, the test is best suited for women with a Gail lifetime risk of 15% or greater or with one or more clinical risk factors for sporadic breast cancer; or for women who do not qualify for a BRCA test or who have had a negative BRCA result and are concerned about their breast cancer risk.5 BREVAGenplus® is not suitable for women with previous diagnoses of lobular carcinoma in situ, ductal carcinoma in situ, or breast cancer, because the Gail model cannot calculate breast cancer risk accurately for such women, or for women with an extensive family history of breast and ovarian cancer.

Phenogen Sciences maintains on its website a list of physicians who have been trained to use BREVAGenplus®. As of March 2015, BREVAGenplus® is available in all states except New York. (BREVAGen® is available in New York.)

Regulatory Status

No SNP-based test to predict breast cancer risk has been approved or cleared by FDA. These tests are offered as laboratory-developed tests under the Clinical Laboratory Improvement Amendments (CLIA) licensed laboratories. Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratories offering such tests as a clinical service must meet general regulatory standards of CLIA and must be licensed by CLIA for high-complexity testing.

FDA has not yet developed specific rules for DTC genetic testing. On November 22, 2013, FDA issued a warning letter to 23andMe ordering the site to “immediately discontinue marketing the Saliva Collection Kit and Personal Genome Service until such time as it receives FDA marketing authorization for the device.” (6) In February 2015, FDA granted marketing authorization to 23andMe for its Bloom syndrome DTC carrier test. 23andMe also provides “ancestry-related genetic reports and uninterpreted raw genetic data only.” (7)

Under current regulations, CLIA requires that laboratories demonstrate the analytical validity of the tests they offer. However, there is no requirement for a test to demonstrate either clinical validity or clinical utility. Some states (e.g., New York) have chosen to regulate DTC laboratories. Because these reviews are not public, the scientific standards applied are unknown.

Scope

Medical policies are systematically developed guidelines that serve as a resource for Company staff when determining coverage for specific medical procedures, drugs or devices. Coverage for medical services is subject to the limits and conditions of the member benefit plan. Members and their providers should consult the member benefit booklet or contact a customer service representative to determine whether there are any benefit limitations applicable to this service or supply. This medical policy does not apply to Medicare Advantage.

Benefit Application

N/A

Rationale

This policy was created in April 2010 and updated periodically with literature review. The most recent update covers the period through March 3, 2015.

Genome-wide association studies (GWAS) examine the entire genome of thousands of subjects for single nucleotide polymorphisms (SNPs), single base-pair variations in the DNA sequence at semi-regular intervals, and attempt to associate variant SNP alleles with particular diseases. Several case-control GWAS, primarily in white women, have investigated common risk markers of breast cancer. In recent years, several SNPs 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. (8-16) Recently, SNPs associated with breast cancer risk in Asian and African women have been the subject of more than a dozen articles, although these appear exploratory. (17-30) GWAS also have identified SNPs in specific genes associated with the onset or severity of chemotherapy-induced toxicity. (31,32)

SNP Panel Tests

As noted in the Background section, 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. The literature on these associations is growing, although information about the risk models is proprietary. 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 commercially available breast cancer risk estimators have been compared with each other to determine if they report similar results on the same individuals, specifically for breast cancer.

Meta-Analyses

Several meta-analyses have investigated the association between breast cancer and various SNPs. Meta-analyses of case control studies have indicated that specific SNPs are associated with increased or decreased breast cancer risk (see Table 2). Other meta-analyses have revealed the interaction between environment (e.g., obesity, age at menarche) (33,34) or ethnicity (35-39) and breast cancer risk conferred by certain SNPs. Zhou et al. (2013) found that a specific polymorphism in the vitamin D receptor gene increased breast cancer risk in African-American but not white women. (40) Breast cancer risk associated with SNPs in microRNAs is commonly modified by ethnicity. (41-44) Meta-analyses of GWAS have identified SNPs at new breast cancer susceptibility loci. (45-47) All of these markers are considered to be in an investigational phase of development.

In 2014, the Breast Cancer Association Consortium published a mega-analysis of 46,450 case patients and 42,461 controls from 38 international meta-analytic studies. (48) The authors assessed 2-way interactions among 3277 breast cancer-associated SNPs. Of 2.5 billion possible 2-SNP combinations, none were statistically significantly associated with breast cancer risk. The study suggests that risk models may be simplified by eliminating interaction terms. Nonetheless, the authors cautioned that despite the large sample size, the study may have been underpowered to detect very small interaction effects, which tend to be smaller than main effects.

In 2014, the Breast and Prostate Cancer Cohort Consortium published 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. (49) Based on published GWAS, 23 SNPs 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 interaction between rs10483813 mutations in RAD51L1 and body mass index (BMI).

Table 2. Results of Meta-Analyses of SNPs and Associations with Breast Cancer

SNP(s)

Association

Study

 

Positive

None

Protective

 

2q35 [rs13387042]

   

Gu 2013 (50)

8q24 [G-allele of rs13281615]

   

Gong 2013 (51)

8q24 [homozygous A-alleles of rs13281615]

   

Gong (2013) (51)

AKAP9 [M463I]

   

Milne (2014) (52)

ATR-CHEK1 checkpoint pathway genesa

 

 

Lin 2013 (53)

ATXN7 [K264R]

   

Milne (2014) (52)

Chemotactic cytokinesb

 

 

Bodelon 2013 (54)

COMT [V158M]

   

He 2012 (55)

COX2 [rs20417]

   

Dai (2014) (56)

COX2 [rs689466]

   

Dai (2014) (56)

COX2 [rs5275]

 

 

Dai (2014) (56)

COX11 [rs6504950]

   

Tang 2012 (57)

CYP1A1 [T3801C]

   

He 2013 (58)

CYP1A2 1F [A-allele of rs762551]

   

Tian 2013 (59)

CYP19 [rs10046]

 

 

Pineda 2013 (60)

Fibroblast growth factor receptor genesc

 

 

kConFab Investigators 2014 (61)

IL-10 [rs1800871]

 

 

Yu 2013 (62)

IRS1 [rs1801278]

   

Zhang 2013 (63)

MAP3K1 [C-allele of rs889312 and G-allele of rs16886165

   

Zheng 2014 (64)

MDM2 [rs2279744]

   

Gao (2014) (65)

MDR1 [C3435T]

   

Wang 2013 (66)

MTR [A2756G]

 

Zhong 2013 (67)

PON1 [L55M],

   

Saadat 2012 (68)

STK15 [F31I]

   

Qin 2013(69)

STK15 [V571I]

 

 

Qin 2013(69)

TCF7L2 [rs7903146]

   

Chen 201 (69)

VDR [rs731236]

   

Perna 2013 (71)

VDR [rs2228570]

   

Zhang (2014) (72)

VEGF [C936T]

 

 

Li (2014) (73)

XRCC2 [R188H]

 

 

He 2014 (74)

XRCC3 [A17893G]

   

He 2012 (75)

XRCC3 [T241M]

   

He 2012 (75)

SNPs: single nucleotide polymorphisms.

a 40 ATR and 50 CHEK1 SNPs genotyped.

b 34 SNPs and groups of SNPs genotyped in 8 chemokine candidate genes: CCL3, CCL4, CCL5, CCL20, CCR5, CCR6, CXCL12, and CXCR4.

c 384 SNPs genotyped in FGFR1, FGFR3, FGFR4, and FGFRL1.

Primary Studies

Because there are no published studies of commercial SNP-based breast cancer risk predictors, published studies of the clinical usefulness of other similar SNP combinations as risk predictors are considered here.

In 2015, McCarthy et al. at the University of Pennsylvania examined the impact of BMI, Gail model risk, and a 12-SNP version of the deCODE BreastCancer™ test on breast cancer risk prediction and biopsy decisions among women with Breast Imaging-Reporting and Data System (BI-RADS) 4 mammograms who had been referred for biopsy (N=464). (76) The original deCODE BreastCancer™ panel included 7 SNPs; neither panel is currently commercially available. 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 SNP panel risk as a continuous variable, a statistically significant association between SNP panel risk and breast cancer diagnosis was observed (odds ratio, 2.30; 95% confidence interval), 1.06 to 4.99; Hosmer-Lemeshow goodness-of-fit test, p=0.035). However, categorized SNP 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, SNP 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 SNP panel, reclassified 9 women (3.4%) below a 2% risk threshold for biopsy, none of whom were diagnosed with cancer.

Aston et al. (2005) evaluated more than 14,000 oligogenotypes, defined by two or more SNPs in 10 breast cancer-associated genes. (77) The association with breast cancer was considered statistically significant for 37 oligogenotypes. The authors observed that oligogenic combinations of 2 to 10 SNPs were strongly associated with wide variation in breast cancer risk; that for many combinations, genes affected breast cancer risk in a manner not predictable from single gene effects; and that compared with individual SNPs, these combinations stratified risk over a broader range.

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. (78) 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 U.K., the risk profile provided by the 7 SNPs 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 SNP 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 (MRI) screening or consideration of more aggressive intervention.

Reeves et al. (2010) evaluated the performance of a panel of seven SNPs associated with breast cancer in 10,306 women with breast cancer and 10,383 without cancer in the U.K. (79) The risk panel also contained 5 SNPs included in the deCODE BreastCancer™ test and used a similar multiplicative approach. Sensitivity studies were performed using only four SNPs and using 10 SNPs, 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%. Of note, simple information on patient histories; e.g., presence of one or two first-degree relatives with breast cancer, provided equivalent or superior risk discrimination (9.1% and 15.4%, respectively).

It is assumed that many more genetic risk markers remain to be discovered because substantial unexplained heritability remains. (80) Researchers from the Collaborative Oncological Gene-Environment Study group, a mega-consortium established to follow-up previous GWAS and candidate gene association studies, estimate that “more than 1000 additional loci are involved in breast cancer susceptibility.” (45) One reason more genetic associations have not been found is that even large GWAS are underpowered to detect uncommon genetic variants.(81)

Two approaches have recently been described to help address this problem. Braun and Buetow (2011) described a technique for multi-SNP analysis of GWAS data based on the study of patient cases selected using their association with known pathways related to disease risk. (82) The authors coined the term Pathways of Distinction Analysis to describe this methodology and demonstrated that using this approach facilitated the identification of disease-related SNPs by creating clusters of similar variants within disease groups that stood out when compared with control groups.

In 2012, Silva et al. reported on the use of DNA pooling methods to aid in detection of genetic polymorphisms.(83) They combined DNA from many individuals (up to 200 patients or controls) into a single sample in an effort to pre-select SNPs of interest in different populations. They concluded that test accuracy was sufficiently robust to allow use of pooling to estimate allelic distributions in populations of interest.

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. (78,79,81,84-87) Whether additional SNP studies are likely to be informative is in 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. Challenges to sorting through the growing literature on this diagnostic approach include nonstandardization and nontransparency of studies. (88) Janssens et al. (2011) published a methods paper providing a road map for optimal reporting and an accompanying detailed article describing good reporting practices.(89)

In 2011, Bloss et al. reported on the psychological, behavioral, and clinical effects of risk scanning in 3639 patients followed for a short time (mean [SD], 5.6 [2.4] months). (90) 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 course of the study itself, no actual increase was observed.

Section Summary

Common SNPs have been shown in primary studies and meta-analyses to be significantly associated with breast cancer risk; some SNPs convey slightly elevated risk compared with the general population risk. Panels of SNPs are commercially available, with results synthesized into breast cancer risk estimates. These have not been clinically validated and clinical utility has not been demonstrated. Non-U.S. tests are commercially available as direct-to-consumer tests. Use of such risk panels for individual patient care or for 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 SNPs. Long-term prospective studies with large sample sizes are needed to determine the clinical validity and utility of SNP-based models for use in predicting breast cancer risk. The discrimination offered by the limited genetic factors currently known is insufficient to inform clinical practice.

Clinical Genetic Tests

OncoVue®

The OncoVue® test was developed by evaluating samples from a large case-control study for 117 common, functional polymorphisms, mostly SNPs, in candidate genes likely to influence breast carcinogenesis. A model using weighted combinations of 22 SNPs in 19 genes together with several Gail model (personal and family history characteristics) risk factors was subsequently identified by multiple linear regression analysis. OncoVue® improved individual sample risk estimation, compared with the Gail model alone (p<0.001), by correctly placing more cases and fewer controls at elevated risk. (91) In the same study, the model was validated on an independent sample set with similarly significant results. To date, this study has only been published in a meeting abstract; no details of the study or its results are available. Note that the Gail model has been shown to accurately estimate the proportion of women (without a strong family history) who will develop cancer in large groups but is a poor discriminator of risk among individuals.(92)

Using the same case control validation data, OncoVue® was also compared with risk estimation determined by 7 SNPs reported in other GWAS (93); the GWAS risk scores were unable to stratify subjects by risk for breast cancer, whereas OncoVue® significantly stratified patients by risk. This study has not been published. Independently, SNPs derived from GWAS are known to result in only low-level estimates of risk at best; in 1 example, a 14-SNP polygenic risk score yielded an odds ratio of only 1.3 for estrogen receptor (ER)‒positive breast cancer and 1.05 for ER-negative breast cancer.(79)

An additional analysis of the same case-control data was reported at the 2010 San Antonio Breast Cancer Symposium. (94) The OncoVue® risk score was calculated in the same discovery set (4768 white women, 1592 cases, 3176 controls) and 2 independent validation sets (1137 white women, 376 cases, 761 controls; 494 African American women, 149 cases, 345 controls). For both OncoVue® and Gail model risk scores, positive likelihood ratios (proportion of patients with breast cancer with an elevated risk estimate [≥20%] divided by the proportion of disease-free subjects with an elevated risk estimate) were calculated. OncoVue® exhibited a 1.6- to 1.8-fold improvement compared with the Gail model in more accurately assigning elevated risk estimates to breast cancer cases rather than controls. At higher risk thresholds, the fold improvement increased and exceeded 2.5 in some sample sets.

Does OncoVue® testing improve the accuracy of breast cancer risk prediction beyond standard risk prediction measures?

The performance of OncoVue® was studied in women from the Marin County, CA, Breast Cancer Adolescent Risk Factor study. A retrospective case control study was developed within the cohort, and samples were evaluated with OncoVue® testing. OncoVue® assigned high-risk status (defined as ≥12% lifetime risk of developing breast cancer) to 19 more women who had had breast cancer (of 169 cases) than did the Gail model, which represented an approximately 50% improvement. (95) OncoVue® was also more effective at stratifying risk in the high-risk Marin County population than 7 SNPs reported in other GWAS. (96) These studies have not yet been published in peer-reviewed journals.

Several supportive studies have been published as meeting abstracts. These address conceptual aspects of the OncoVue® test but do not appear to report data using the final OncoVue® test configuration. One fully published study characterizes SNPs that exhibit breast cancer risk associations that vary with age. (97) This study stratified breast cancer cases and normal controls into three age groups, then determined breast cancer risk for SNP homozygotes and heterozygotes for each of 18 candidate SNPs within each age group. Of these, 5 SNP variants had statistically significant odds ratios for at least 1 age group. In a separate validation sample, only 1 had a statistically significant odds ratio but not in a pattern similar to that of the discovery set. The other 4 SNPs, although not significant, were judged to have patterns of results similar to that of the discovery set and were investigated further by a sliding 10-year window strategy; the authors suggested that results of this analysis clarified age-specific breast cancer risk associations. The authors noted the need for additional validation in other populations and nonwhite ethnicities.

Do results of OncoVue® testing lead to changes in management that result in health outcome improvements?

The medical management implications of this test are unclear. The Gail model was originally designed for use in clinical trials, not for individual patient care and management. (98) Thus, using the Gail model as a baseline for comparison may not be sufficiently informative. Additionally, no evidence of improved outcomes as a result of management changes in OncoVue®-identified high-risk patients has been presented or published. The OncoVue® sample report makes no recommendations regarding patient management. The Progressive Medical Enterprises website makes this statement regarding test results: “Women with Moderate or High breast cancer risk level scores should work closely with their healthcare provider to establish appropriate screening regimens, as well as to consider more sophisticated screening techniques, the use of cancer prevention medications, and implementing specific lifestyle changes to proactively manage their breast cancer risk.” (2)

A pilot study using buccal samples from women in the Marin County, CA retrospective case control study previously described aimed to examine the genotypes of subjects determined to be high risk (≥12%) by OncoVue®.(99) Of 22 SNPs assessed by the OncoVue® assay, one (rs7975232 in the vitamin D receptor gene) occurred significantly more often in high-risk cases than in the overall (all cases plus controls) sample (64% vs. 34%; p<0.001); however, the incidence among all cases (29%) was less than that among controls (39%). The authors postulated a potential prevention strategy using vitamin D supplementation in women with this genotype. Although recent retrospective studies support an association between sunlight exposure, elevated serum levels of vitamin D (25[OH]D)/vitamin D supplementation, and reduced risk of breast cancer, prospective uncontrolled studies gave mixed results (positive or no association). (100,101) Clinical trials demonstrating improved health outcomes in patients identified as high risk due to OncoVue detection of the rs7975232 SNP who were subsequently treated with vitamin D supplementation have not been reported.

BREVAGen® and BREVAGenplus®

In 2010, Mealiffe et al. published a clinical validation study of the BREVAGen® test. (87) The authors evaluated a 7-SNP panel in a nested case control cohort of 1664 case patients and 1636 controls. A model that multiplied the individual risks of the 7 SNPs 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 SNPs with the Gail model resulted in a modest improvement in classification of breast cancer risks, but area under the curve (AUC) only increased from 0.557 to 0.594 (0.50 represents no discrimination, 1.0 perfect discrimination). The impact of reclassification on 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.

Information about analytic validity of the BREVAGenplus® test is provided in the published study, but is indeterminate. Genomic DNA samples were analyzed on custom oligonucleotide arrays (Affymetrix, Santa Clara, CA). Mean concordance across duplicate samples included for quality control was 99.8%; breast cancer loci had call rates (a measure of SNP detection) above 99%. For approximately 70% of samples with sufficient DNA available, whole genome amplification also was carried out using the Sequenom (San Diego, CA) MassARRAY platform. Across samples that had not been excluded for lack of DNA or poor quality data (proportion not reported), concordance between the 2 assays was 97%, and the resulting call rate was 96.8%. Genotype data for 121 samples that had 1 or more inconsistencies between the Sequenom analysis, and the corresponding custom array genotype were excluded. Conflicting calls were not differentially distributed across case patients and controls. The authors acknowledged that the 2 assays performed “relatively poorly,” but asserted that consensus calls were nonetheless accurate.

In 2013, Dite et al. published a similar case control study of the same 7 SNPs assuming the same multiplicative model (based on independent risks of each SNP). (102) Predictive ability of the Gail model with and without the 7 SNP panel was compared in 962 case patients and 463 controls, all 35 years of age or older (mean age, ≈45 years). AUC of the Gail model was 0.58 (95% CI: 0.54 to 0.61); in combination with the 7-SNP panel, AUC increased to 0.61 (95% CI: 0.58 to 0.64; bootstrap resampling, 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-SNP 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 addition of the 7-SNP panel to the Gail model improved predictive accuracy, the magnitude of improvement is small, overall accuracy is moderate, and impact on health outcomes is uncertain.

Other Clinical Genetic Tests

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

  • Zheng et al. (103) found that 8 SNPs, combined with other clinical predictors, were significantly associated with breast cancer risk; the full model gave an AUC of 0.63.
  • Campa et al. (104) evaluated 17 SNP breast cancer susceptibility loci for any interaction with established risk factors for breast cancer but found no evidence that the SNPs modified the associations between established risk factors and breast cancer.
  • Wacholder et al. (84) evaluated the performance of a panel of 10 SNPs associated with breast cancer that had, at the time of the study, been validated in at least 3 published GWAS. 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 SNP 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). Mammographic density also was not included. The authors found that adding the SNP panel to the Gail model resulted in slightly better stratification of a woman’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 assigned to the top 20% risk group. AUC for the combined SNP and Gail model was 62% (50% is random, 100% is perfect).
  • Darabi et al. (105) investigated the performance of 18 breast cancer risk SNPs, together with mammographic percentage density (PD), BMI, and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well-characterized case-control study of postmenopausal Swedish women. Performance of a risk prediction model based on an initial set of 7 breast cancer risk SNPs was improved by including 11 more recently established breast cancer risk SNPs (p<0.001). Adding mammographic PD, BMI and all 18 SNPs 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 SNPs, would capture 10% more cases. Impacts on net health outcomes from such a change are unknown.
  • Armstrong et al. (106) examined the impact of pretest breast cancer risk prediction on the classifıcation of women with an abnormal mammogram above or below the risk threshold for biopsy. Currently, 1-year probability of breast cancer among women with BI-RADS category 3 mammograms is 2%; these women undergo 6-month follow-up rather than biopsy. In contrast, women with BI-RADS4 mammograms have a 6% (BI-RADS 4A) or greater (BI-RADS 4B and 4C) probability of developing breast cancer in 1 year; these women are referred for biopsy. Using the Gail model plus 12 SNPs for risk prediction and a 2% biopsy risk threshold, 8% of women with a BI-RADS 3 mammogram were reclassified above the threshold for biopsy and 7% of women with BI-RADS 4A mammograms were reclassified below the threshold. The greatest impact on reclassification was attributed to standard breast cancer risk factors. Net health outcomes were not compared between women who were reclassified and those who were not.

Although results of these studies support the concept of clinical genetic tests, they do not represent direct evidence of their clinical validity or utility.

Section Summary

There is a lack of published detail regarding OncoVue® and BREVAGenplus® test validation, supportive data, and management implications. Available data suggest that OncoVue® and BREVAGenplus® may add predictive accuracy to the Gail model. However, the degree of improved risk prediction may be modest, and clinical implications are unclear. There is insufficient evidence to determine whether using breast cancer risk estimates from OncoVue® or BREVAGenplus® in asymptomatic individuals changes management decisions and improves patient outcomes.

Ongoing and Unpublished Clinical Trials

Some currently unpublished trials that might influence this policy are listed in Table 3.

Table 3. Summary of Key Trials

NCT No.

Trial Name

Planned Enrollment

Completion Date

Ongoing

     

NCT00329017a

Correlation of SNP Patterns With Fine Needle Aspiration Cytomorphology in High Risk Postmenopausal Women

500

May 2015

NCT00122239

A Study of Gene Polymorphisms and Normal Tissue Radiation Injury in Patients Treated for Breast, Prostate, Brain, Lung, and Head and Neck Cancers

500

Feb 2014b

NCT: national clinical trial.

a Denotes industry-sponsored or cosponsored trial.

b Final data collection date for primary outcome measure

Summary of Evidence

Clinical utility of single nucleotide polymorphisms (SNP) panel tests and clinical genetic tests (e.g., OncoVue®, BREVAGenplus®) is unknown. Information about analytic performance (reproducibility) of marketed tests is lacking. Most tests are in an investigational phase of development, having demonstrated associations between the SNPs tested and breast cancer risk. Clinical genetic tests may improve predictive accuracy of currently used clinical risk predictors. However, the magnitude of improvement is small, and clinical significance is uncertain. Whether potential harms of these tests due to false negative and false positive results are outweighed by potential benefit associated with improved risk assessment is unknown. Use of these tests is therefore considered investigational.

Practice Guidelines and Position Statements

National Comprehensive Cancer Network (NCCN)

Current guidelines from the NCCN identify the following limitations of multigene cancer panels: unknown significance of some variants, uncertain level of risk associated with most variants, and unclear guidance on risk management for carriers of some variants. (107) For breast cancer risk assessment, the American Society of Clinical Oncology recommends the Gail model(108) or risk models for women with elevated risk based on family history (e.g., Claus et al. (109) or Tyrer-Cuzick et al. (110). (107,111)

U.S. Preventive Services Task Force Recommendations

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

Medicare National 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.

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Coding

Codes

Number

Description

CPT

81599

Unlisted multianalyte assay with algorithmic analysis

 

84999

Unlisted chemistry procedure

Type of Service

Laboratory

 

Place of Service

Laboratory

 

Appendix

N/A

History

Date

Reason

07/12/11

Add to Pathology/Laboratory Section - New medical policy with literature search through March 2011; considered investigational. ICD-10 codes included.

05/24/12

Policy renumbered to 12.04.63 (previously 2.04.63) and reassigned to new Genetic Testing category.

07/20/12

Replace policy. Policy updated with literature search, references 12, 13, 19-22, 27-30 added. No change to policy statement.

10/10/12

Update Coding Section – ICD-10 codes are now effective 10/01/2014.

01/11/13

Effective 1/1/13, CPT codes 81200 – 81479 will be used for lab testing; these replace codes 83891 – 83912 which are deleted effective 12/31/12.

05/14/13

Update Related Policies. Add 12.04.91.

07/24/13

Replace policy. Policy updated with literature search through April 9, 2013; references 23-33 added. No change to policy statement.

10/17/13

Update Related Policies. Change title to 12.04.57.

11/21/13

Update Related Policies. Add 12.04.93.

01/16/14

Update Related Policies. Change title to 12.04.504.

06/09/14

Annual Review. Policy updated with literature review through March 18, 2014. Policy combined with Policy No. 12.04.57 (Non-BRCA Breast Cancer Risk Assessment [e.g., OncoVue®]) and title changed to “Use of Common Genetic Variants (SNPs) to Predict Risk of Nonfamilial Breast Cancer;”; references 1-2, 6-8, 34-45, 48-50, 52-53, 56-64, 66-67, 69-70, 74, and 96 added. Investigational policy statement for OncoVue® and BREVAGen® modified to indicate investigational for all indications.

07/23/14

Update Related Policies. Remove 12.04.91.

06/17/15

Annual Review. Policy updated with literature review through March 3, 2015. References 1, 2, 49, 52, 56, 65, 72, 73, 76, 108 added; references 1 and 4-5 deleted. Policy statements unchanged.


Disclaimer: This medical policy is a guide in evaluating the medical necessity of a particular service or treatment. The Company adopts policies after careful review of published peer-reviewed scientific literature, national guidelines and local standards of practice. Since medical technology is constantly changing, the Company reserves the right to review and update policies as appropriate. Member contracts differ in their benefits. Always consult the member benefit booklet or contact a member service representative to determine coverage for a specific medical service or supply. CPT codes, descriptions and materials are copyrighted by the American Medical Association (AMA).
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