Medical Policy: 02.04.44
Original Effective Date: August 2013
Reviewed: April 2019
Revised: April 2019
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This Medical Policy document describes the status of medical technology at the time the document was developed. Since that time, new technology may have emerged or new medical literature may have been published. This Medical Policy will be reviewed regularly and be updated as scientific and medical literature becomes available.
A number of single nucleotide variants (SNVs), 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 a number SNVs 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 this type of test is to identify those individuals at increased risk who may benefit from more intensive surveillance.
Several common single nucleotide variants(SNVs) associated with breast cancer (see below table) 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, 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.
|SNVs Studied in Association with Breast Cancer Risk|
|8q24 [G-allele of rs13281615]|
|8q24 [homozygous A-alleles of rs13281615]|
|em>ATR-CHEK1 checkpoint pathway genes|
|CYP1A2 1F [A-allele of rs762551]|
|Fibroblast growth factor receptor genes|
|MAP3K1 [C-allele of rs889312 and G-allele of rs 16886165|
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:
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 had breast cancer (non-familial).
The intervention of interest is testing for common single nucleotide variants (SNVs) associated with a small increase in the risk of breast cancer.
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.
The outcomes of interest are a reclassification of individuals from normal risk and evidence of a change in management (eg, 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.
Patients who are asymptomatic and at average risk of breast cancer by clinical criteria are actively managed by internists in an outpatient clinical setting.
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.
Reeves et. a.l (2010) evaluated the performance of a panel of 7 SNPs associated with breast cancer in 10,306 women with breast cancer and 10,383 without cancer in the U.K. 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 4 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%. 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).
In 2010, Mealiffe et. al. published a clinical validation study of the BREVAGen test. 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 the 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.
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). The 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% confidence interval [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.
A 2015 study by Allman et. al. included 7539 African American and 3363 Hispanic women from the Women’s Health Initiative. 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.
In 2015, Mavaddat et. al reported a multicenter study that assessed risk stratification using 77 breast cancer associated SNPs in 33,673 breast cancer cases and 33,381 control women of European descent. 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). Lifetime risk of breast cancer was16.6% for women in the highest quintile of the risk score compared with 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 SNPs 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.
Although results of these studies support the concept of clinical genetic tests, they do not represent direct evidence of their clinical validity or utility.
Common single nucleotide variants (SNVs) have been shown in primary studies and meta-analyses 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 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. SNV panel tests have few 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.
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 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.
One potential use of SNV testing is to evaluate the risk of breast cancer for chemoprevention. In 2017, Cuzick et al assessed whether a panel of 88 SNPs could improve risk prediction over traditional risk stratification using data from 2 randomized tamoxifen prevention trials. The study included 359 cases and 636 controls, with the 88 SNPs assessed on an Illumina OncoArray that evaluated approximately half a million SNPs. The primary outcome was breast cancer or ductal carcinoma in situ. The 88 SNP score improved discriminability above the Tyrer-Cuzick risk evaluator; however, there was 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 SNP score was added. The SNP score did not predict which women would benefit from tamoxifen.
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, 5.6 months). 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.
In 2015, McCarthy et. al. examined the impact of BMI, Gail model risk, and a 12-SNV version of the deCODE BreastCancer 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 BreastCancer panel included 7 SNPs; 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 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% CI, 1.06 to 4.99; p=0.035). However, categorized SNP panel risks (eg, 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 (3.4%) women below a 2% risk threshold for biopsy, none of whom were diagnosed with cancer.
The number of common low-penetrance single nucleotide variants (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.
There is insufficient evidence to determine whether using SNVs for breast cancer risk in asymptomatic individuals changes management decisions and improves patient outcomes.
The OncoVue® Breast Cancer Risk Test (InterGenetics™ Inc., Oklahoma City, OK) is a proprietary test that evaluates multiple, low risk single nucleotide polymorphisms (SNPs) associated with breast cancer. The 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. 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. The test is stated to be “an aid in the qualitative assessment of breast cancer risk…not intended as a stand-alone test for the determination of breast cancer risk in women.”
For women without a strong family history of breast cancer and at average risk prior to testing, OncoVue® purports to estimate a woman’s individual risk and place her in standard, moderate or high risk groups. The results are intended to help a woman and her physician decide if more frequent exams and/or more sophisticated surveillance techniques are indicated. For women already known to be at high risk based on a family history consistent with hereditary breast cancer, the test is represented as having added value by indicating greater or lesser risk at different life stages.
The OncoVue® test is available only through the Breast Cancer Risk Testing Network (BCRTN), described as a network of Breast Care Centers engaged in frontline genetic identification of breast cancer risk levels in their patients. BCRTN member centers will provide genetic breast cancer risk testing for their patients using OncoVue® as part of a comprehensive education program to help OncoVue® “at-risk” women understand their risk level and intervention strategies. BCRTN members will be selected for the network based on a number of criteria, including quality standards of care, level of breast cancer surveillance technology, and the capability of providing patient education on genetic testing and future risk management protocols. Participating centers located throughout the United States is listed on the OncoVue® website. OncoVue® is not listed in the Genetic Testing Registry of the National Center for Biotechnology Information.
On October 6, 2014 Phenogen Sciences, Inc. Charlotte, NC, announced the availability of BREVAGenplus™. This test is an enhancement of the company’s first generation product BREVAGen™ that included 7 SNPs. BREVAGenplus™ includes a greatly expanded SNP panel (over 70) and is applicable for additional ethnicities African-American, Caucasian and Hispanic.
BREVAGenplus™ predictive risk test is performed in a physician’s office using a simple, non-invasive cheek swab. The test combines information from the patient’s genetic markers (SNPs) known to be associated with sporadic breast cancer, with their clinical risk score which includes factors such as the patient’s current age, age at menarche, age at first live birth, race/ethnicity, and having first degree relatives with breast cancer (if any)to calculate their risk of developing sporadic breast cancer. This clinical risk score is determined by the National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model. The test provides five year and lifetime predictive risk assessments to more accurately determine the patient’s risk of developing breast cancer during those time frames. This assists the physician in developing a personalized breast cancer risk management plan by putting the appropriate surveillance measures in place.
Suitable candidates for BREVAGenplus™ testing include African American, Caucasian and Hispanic women aged 35 years and older; women with an above average clinical risk score (Gail lifetime risk) of 15% or greater; women with one or more clinical risk factors for sporadic breast cancer; women who do not qualify for a BRCA test or who have had a negative BRCA result; women concerned about their breast cancer risk. This testing is not suitable for woman who have had a previous diagnosis of breast cancer, lobular carcinoma in situ (LCIS) or ductal carcinoma in situ (DCIS).
Phenogen Sciences maintains on its website a list of physicians by state who have been trained to use BREVAGenplus™. If a state does not have a provider listed they advise to contact BREVAGen to find out how the physician can order BREVAGenplus.
Information about the analytic validity of the BREVAGenplus was provided in a published study by Mealiffe et al (2010), but is indeterminate. Genomic DNA samples were analyzed on custom oligonucleotide arrays (Affymetrix, Santa Clara, CA). The mean concordance across duplicate samples included for quality control was 99.8%; breast cancer loci had call rates (a measure of SNV detection) above 99%. For approximately 70% of samples with sufficient DNA available, whole genome amplification 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.
Evidence of the analytic validity of the BREVAGenplus is limited. Discordance between BREVAGenplus and an orthogonal technology was noted in a published study. The analytic validity of BREVAGenplus is therefore uncertain.
For individuals who are asymptomatic and at average risk of breast cancer by clinical criteria who receive testing for common single nucleotide variants (SNVs) associated with a small increase in the risk of breast cancer, or with OncoVue® Breast Cancer Risk Test or BREVAGenplusTM to predicti risk, the evidence is insufficient based on the peer reviewed medical literature. Clinical genetic tests may improve the predictive accuracy of currently used 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 SNP-based models for use in predicting breast cancer risk. The discrimination 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.
In 2017, the American College of Obstetricians and Gynecologists issued a practice bulletin (number 179, replaces practice bulletin number 122 August 2011), regarding breast cancer risk assessment and screening in average risk women which states the following:
Clinical Considerations and Recommendations:
No test combining the results of SNV analysis with clinical factors to predict breast cancer risk has been approved or cleared by the U.S. Food and Drug Administration (FDA). These are offered as laboratory-developed tests; that is, tests developed and used at a single testing site. Laboratory developed tests, as a matter of enforcement discretion, have not been traditionally regulated by FDA in the past. They do require oversight under the Clinical Laboratory Improvement Amendments of 1988 (CLIA), and the development and use of laboratory developed tests is restricted to laboratories certified as high complexity under CLIA.
Under the current regulatory program, 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.
Testing for one or more single nucleotide variants (SNVs) to predict an individual's risk of breast cancer is considered investigational as the evidence is insufficient to determine the effects of the technology on net health outcomes.
OncoVue® or 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 as the evidence is insufficient to determine the effects of the technology on net health outcomes.
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