Medical Policy: 02.04.44
Original Effective Date: August 2013
Reviewed: April 2021
Revised: April 2021
This policy contains information which is clinical in nature. The policy is not medical advice. The information in this policy is used by Wellmark to make determinations whether medical treatment is covered under the terms of a Wellmark member's health benefit plan. Physicians and other health care providers are responsible for medical advice and treatment. If you have specific health care needs, you should consult an appropriate health care professional. If you would like to request an accessible version of this document, please contact customer service at 800-524-9242.
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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.
Note: This policy is not intended to address testing for a known familial variant for breast cancer, or testing of patient at high risk based on family history.
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 (1%). There are commercially available assays that test for a number SNVs to predict an individual’s risk of breast cancer relative to the general population which may 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 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. 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.
Breast cancer is the most common malignancy in women in the United States and is second only to lung cancer as a cause of cancer death. The American Cancer Society has estimated that 284,200 Americans will be diagnosed with breast cancer and 44,130 will die of the disease in the United States in 2021.
Breast cancer risk is strongly associated with both genetic and environmental factors. For non-familial breast cancer, the Gail Model has been commonly used to produce individual risk estimates. The model incorporates individual risk factors including age, family history (breast cancer among first-degree relatives), personal reproductive history (age at menarche and at first live birth), and personal medical history (number of previous breast biopsies and presence of biopsy confirmed atypical hyperplasia) to identify individuals who have an increased 5-year risk and lifetime risk of invasive breast cancer and may benefit from breast cancer risk reduction interventions such as risk-reduction agents (i.e., tamoxifen, raloxifene, anastrozole, exemestane) or risk-reduction surgery (risk-reducing mastectomy [RRM]).
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:
Commercially available assays purportedly test for a number of SNVs and predict an individual’s risk of breast cancer relative to the general population. Some of these assays incorporate clinical information into risk prediction algorithms.
Examples of genetic testing assays for non-familial breast cancer risk assessment include but are not limited to the following:
The OncoVue Breast Cancer Risk Test (InterGenetics, Inc., Oklahoma City, OK) is a proprietary test that evaluates multiple, low risk single nucleotide variants (SNVs) 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 and 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 are 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 BREVEGen that included 7 single nucleotide variants (SNVs). BREVAGenplus includes a greatly expanded SNV 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 single nucleotide variants (SNVs) 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.
The TruSight Cancer Sequencing Panel (Illumina), targets 94 genes suspected to play a role in predisposing to cancer, including genes associated with both common (e.g., breast, colorectal) and rare cancers. In addition, the panel includes 284 SNVs suspected to be associated with cancer through genome-wide association studies (GWAS).
The Infinium OncoArray-500K BeadChip is a 24-sample format Illumina array with content drawing on many features of the Collaborative Oncological Gen-environmental Study (iCOGS) array1. The OncoArray offers the most comprehensive, highest-density BeadChip available for researching cancer predisposition and risk. The OncoArray contains 500,000 SNVs with genome wide backbone of 275,000 tag SNVs. Additional SNVs include genetic variants associated with breast, colorectal, lung, ovarian and prostate cancers.
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. Patients who are asymptomatic and at average risk of breast cancer by clinical criteria are actively managed in an outpatient clinical setting.
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.
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).
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.
Studies have analyzed the potential impact of adding genetic information from a panel of SNVs associated with breast cancer risk to the Gail Model. These studies showed modest (limited) clinical gains in reclassification of risk. These studies have either been theoretical in nature or they combined model building with evaluation which may complicate evaluating the result in clinical context.
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 2018, Rudolph et.al., evaluated joint associations of a 77-single nucleotide polymorphism (SNP) polygenic risk scores (PRS) with reproductive history, alcohol consumption, menopausal hormone therapy (MHT), height and body mass index (BMI). They tested the null hypothesis of multiplicative joint associations for PRS and each of the environmental factors and performed global and tail-based goodness-of-fit tests in logistic regression models. The outcomes were breast cancer overall and by estrogen receptor (ER) status. The study sample comprised 28,239 cases and 30,445 controls of European ancestry from 20 studies: two case-control studies nested in prospective cohorts, eight population-based case-control and 10 non-population based case-control studies, all participating in the Breast Cancer Association Consortium (BCAC). Eligible studies had at least 200 cases and 200 controls, with genotype data and information on at least one of the environmental risk factors of interest. Studies that oversampled cases with family history of breast cancer were excluded. The strongest evidence for a non-multiplicative joint associations with the 77-SNP PRS was for alcohol consumption (P-interaction = 0.009), adult height (P-interaction = 0.025) and current use of combined MHT (P-interaction = 0.038) in ER-positive disease. Risk associations for these factors by percentiles of PRS did not follow a clear dose-response. In addition, global and tail-based goodness of fit tests showed little evidence for departures from a multiplicative risk model, with alcohol consumption showing the strongest evidence for ER-positive disease (P = 0.013 for global and 0.18 for tail-based tests). The combined effects of the 77-SNP PRS and environmental risk factors for breast cancer are generally well described by a multiplicative model. Larger studies are required to confirm possible departures from the multiplicative model for individual risk factors and assess models specific for ER-negative disease.
In 2019, Kapoor et. al., performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions. Analyses was performed using 28,176 cases and 32,209 controls genotyped with iCOGS array and 44,109 cases and 48,145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the note worthiness of the meta-analyzed array-specific interactions. Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR) = 0.85 (0.78-0.93), P = 2.8 x 104] and overall breast cancer [OR = 0.85 (0.78-0.92), P = 7.4 x 105) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (P = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth. The authors concluded that our study provides the most comprehensive evaluation to date of potential effect modification of all known common genetic susceptibility variants by environmental risk factors for breast cancer. Our findings are based on the largest available dataset on breast cancer. Despite its large sample size, the study may remain statistically underpowered, considering the rather modest effect sizes of most of the common variants associated with breast cancer risk and further limitation of our study is that the findings may not be generalizable to other racial/ethnic groups since the analyses were restricted to women of European ancestry. Overall, the results from our analyses do not suggest strong effect modification of the association between breast cancer susceptibility loci and risk of breast cancer by established epidemiological risk factors.
For individuals who are asymptomatic and at average risk of breast cancer by clinical criteria (no family history/non-familial) who received testing with common single nucleotide variants (SNVs) the evidence includes systematic reviews and observational studies. Some SNV’s 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. No randomized controlled trials evaluating the clinical utility of SNV panel testing to predict the risk of breast cancer were identified. Randomized controlled prospective trials with large sample sizes are needed to determine the clinical validity and utility of SNV-based models for use in predicting breast cancer risk. The U.S. Preventive Services Task Force and American Cancer Society recommend mammography for breast cancer screening in average risk individuals. There is a lack of high-quality evidence to determine if SNV-based risk assessment has any impact on health care outcomes and therefore, 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:
All cancers develop as a result of mutations in certain genes, such as those involved in the regulation of cell growth and/or DNA repair, although not all of these mutations are inherited from a parent. For example, sporadic mutations can occur in somatic/tumor cell only, and de novo mutations can occur for the first time in a germ cell (i.e., egg or sperm) of in the fertilized egg itself during early embryogenesis.
Cancer genetic risk assessment and genetic counseling is a multi-step processing involving the identification and counseling of individuals at risk for familial or hereditary cancer.
Testing should be considered in individuals for whom there is a personal or family history suggesting genetic cancer susceptibility and for whom results will aid in risk management and treatment. The selection of appropriate candidates for genetic testing is based on personal and familial characteristics that determine the individual’s prior probability of being a carrier of a pathogenic or likely pathogenic variant, and on the psychosocial degree of readiness of the person to receive genetic test results. The genetic testing strategy is greatly facilitated when a pathogenic or likely pathogenic variant has already been identified in another family member.
For women not considered to be at risk for familial/hereditary breast cancer, an evaluation of other elements of risk that contribute to increased breast cancer risk is recommended. These include demographic factor such as female gender, age, and ethnicity/race.
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. 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 testing.
Note: This policy is not intended to address testing for a known familial variant for breast cancer or testing of patient at high risk based on family history.
See related medical policy:
Non-familial genetic testing of one more single nucleotide variants (SNVs) utilized as a method of estimating an individual’s risk of developing breast cancer is considered investigational, these include, but are not limited to the following:
For individuals who are asymptomatic and at average risk of breast cancer by clinical criteria (no family history/non-familial) who received testing with common single nucleotide variants (SNVs) there is a lack of high-quality evidence to determine if single nucleotide variant (SNV) based risk assessment has any impact on health care outcomes and therefore, the evidence is insufficient to determine the effects of the technology on health outcomes.
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