Medical Policy: 02.04.44 

Original Effective Date: August 2013 

Reviewed: April 2018 

Revised: April 2018 

 

Benefit Application:

Benefit determinations are based on the applicable contract language in effect at the time the services were rendered. Exclusions, limitations or exceptions may apply. Benefits may vary based on contract, and individual member benefits must be verified. Wellmark determines medical necessity only if the benefit exists and no contract exclusions are applicable. This medical policy may not apply to FEP. Benefits are determined by the Federal Employee Program.

 

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.

 

Description:

A number of 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 a number 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 this type of test is to identify those individuals at increased risk who may benefit from more intensive surveillance.

 

Several common single nucleotide polymorphisms (SNPs) 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 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.

 

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 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). 

 

Clinical Validity

Genome-wide association studies (GWAS) examine the entire genome of thousands of subjects for SNPs at semiregular 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. A number of SNPs associated with breast cancer have been reported at a high level of statistical significance and have been validated in two or more large, independent studies. SNPs associated with breast cancer risk in Asian and African women have been the subject of more than a dozen articles.

 

A number of meta-analyses have investigated the association between breast cancer and individual SNPs. Meta-analyses of case-control studies have indicated that specific SNPs are associated with increased or decreased breast cancer risk (see Table below). Other meta-analyses have revealed the interaction between environment (e.g., obesity, age at menarche) or ethnicity and breast cancer risk conferred by certain SNPs. 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. Breast cancer risk associated with SNPs in microRNAs is commonly modified by ethnicity. Meta-analyses of GWAS have identified SNPs at new breast cancer susceptibility loci. All of these markers are considered to be in an investigational phase of development.

SNPs Studied in with Breast Cancer Risk

  • 2q35 [rs13387042]
  • 8q24 [G-allele of rs13281615]
  • 8q24 [homozygous A-alleles of rs13281615]
  • AKAP9 [M463I]
  • ATR-CHEK1 checkpoint pathway genes
  • ATXN7 [K264R]
  • Chemotactic cytokines
  • COMT [V158M]
  • COX2 [rs20417]
  • COX2 [rs689466]
  • COX2 [rs5275]
  • COX11 [rs6504950]
  • CYP1A1 [T3801C]
  • CYP1A2 1F [A-allele of rs762551]
  • CYP19 [rs10046]
  • Fibroblast growth factor receptor genes
  • IL-10 [rs1800871]
  • IRS1 [rs1801278]
  • MAP3K1 [C-allele of rs889312 and G-allele of rs 16886165
  • MDM2 [rs2279744]
  • MDR1 [C3435T]
  • MTR [A(2756G]
  • PON1 [L55M]
  • STK15 [F31I]
  • STK15 [V571I]
  • TCF7L2 [rs7903146]
  • VDR [rs731236]
  • VDR [rs2228570]
  • VEGF [C936T]
  • XRCC2 [R188H]
  • XRCC3 [A17893G]
  • XRCC3 [T241M]

 

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.

  • Zheng et. al. (2010) found that 8 SNPs, combined with other clinical predictors, were significantly associated with breast cancer risk; the full model gave an AUC of 0.63.77
  • Campa et. al. (2011) evaluated 17 SNP breast cancer susceptibility loci for any interaction with established risk factors for breast cancer but found no evidence that the SNVs modified the associations between established risk factors and breast cancer.
  • Wacholder et. al. (2010) 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 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. (2012) investigated the performance of 18 breast cancer risk SNPs, 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. 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 percentage density, 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 (2013) 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 Breast Imaging-Reporting and Data System (BI-RADS) category 3 mammograms is 2%; these women undergo 6-month follow-up rather than biopsy. In contrast, women with BI-RADS category 4 mammograms have a 6% (BI-RADS category 4A) or greater (BI-RADS 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 a BI-RADS category 3 mammogram were reclassified above the threshold for biopsy, and 7% of women with BI-RADS category 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.

 

Summary: Clinical Validity

Common single nucleotide polymorphisms (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. 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 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. 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 SNPs. 

 

Clinical Utility 

One potential use of SNP 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.

 

Summary: Clinical Utility

The number of common low-penetrance single nucleotide polymorphisms (SNPs) associated with breast cancer is rapidly increasing. No studies were identified that provide direct evidence that use of SNP-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.

 

OncoVue® and BREVAGenplus™ 

OncoVue®

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.

 

BREVAGenplus™ 

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.

 

Summary of Evidence  

For individuals who are asymptomatic and at average risk of breast cancer by clinical criteria who receive testing for common single nucleotide polymorphisms (SNPs)  associated with a small increase in the risk of breast cancer, the evidence includes observational studies. Information about analytic performance (reproducibility) of currently marketed tests is lacking. 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 SNPs 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.

 

Practice Guidelines and Position Statements

American College of Obstetricians and Gynecologists

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:

  • How should individual breast cancer risk be assessed?
    • Health care providers periodically should assess breast cancer risk by reviewing the patient’s history. Breast cancer risk assessment is based on combination of the various factors that can affect risk. Initial assessment should elicit information about reproductive risk factors, results of prior biopsies, ionizing radiation exposure and family history of cancer. Health care providers should identify cases of breast, ovarian, colon, prostate, pancreatic, and other types of germline mutation associated cancer in first-degree, second-degree and possibly third-degree relatives as well as the age of diagnosis. Women with potentially increased risk of breast cancer based on initial history should have further risk assessment. Assessments can be conducted with one of the validated assessment tools available online, such as the GAIL, BRCAPRO, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, International Breast Cancer Intervention Studies (IBIS, also known as Tyrer-Cuzick), or the Claus model.
    • Risk assessment is important to determine if a woman is at average or increased risk of breast cancer to guide counseling regarding breast cancer surveillance, risk reduction and genetic testing. Risk assessment should not be used to consider a woman ineligible for screening appropriate for her age. Rather risk assessment should be used to identify women who may benefit from genetic counseling, enhanced screening such as magnetic resonance imaging screening, more frequent clinical breast examinations, or risk-reduction strategies.

 

Regulatory Status

No test combining the results of SNP 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.

 

Prior Approval:

 

Not applicable.

 

Policy:

Testing for one or more single nucleotide polymorphisms (SNPs) 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.

 

The 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.

 

Procedure Codes and Billing Guidelines:

To report provider services, use appropriate CPT* codes, Alpha Numeric (HCPCS level 2) codes, Revenue codes, and/or diagnosis codes.

  • 81479 Unlisted molecular pathology procedure
  • 81599 Unlisted multianalyte assay with algorithmic analysis

 

Selected References:

  • BREVEGen
  • OncoVue
  • National Comprehensive Caner Network (NCCN): NCCN Guidelines Version 1.2013 Breast Cancer Risk Reduction, Discussion
  • American Cancer Society Breast Cancer Genetics: Is Testing an Option?
  • American Society of Clinical Oncology Policy Statement Update: Genetic and Genomic Testing for Cancer Susceptibility; Journal of Clinical Oncology, February 10, 2010 vol. 28 no 5 893-901
  • Assessment of Clinical Validity of a Breast Cancer Risk Modeling Combining Genetics and Clinical Information; Journal National Cancer Institute, 2010 Nov 3; 102(21): 1618-27
  • 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 2010; 304(4):426-34
  • Wacholder S, Hartge P, Prentice R et al. Performance of common genetic variants in breast-cancer risk models N Engl J Med 2010; 362(11):986-93
  • 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 2014; 23(7):1934-46.
  • National Comprehensive Cancer Network (NCCN),  Breast Cancer Risk Reduction Version 1.2015.
  • National Comprehensive Cancer Network (NCCN), Genetic/Familial High-Risk Assessment: Breast and Ovarian Version 2.2016.
  • BREVAGenplus
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Policy History:

  • April 2018 - Annual review, Policy revised
  • April 2017 - Annual review, Policy revised
  • April 2016 - Annual review, Policy revised
  • May 2015 - Annual review, Policy revised
  • June 2014 - Annual review, Policy revised
  • August 2013 - New policy

 

Wellmark medical policies address the complex issue of technology assessment of new and emerging treatments, devices, drugs, etc.   They are developed to assist in administering plan benefits and constitute neither offers of coverage nor medical advice. Wellmark medical policies contain only a partial, general description of plan or program benefits and do not constitute a contract. Wellmark does not provide health care services and, therefore, cannot guarantee any results or outcomes. Participating providers are independent contractors in private practice and are neither employees nor agents of Wellmark or its affiliates. Treating providers are solely responsible for medical advice and treatment of members. Our medical policies may be updated and therefore are subject to change without notice.

 

*CPT® is a registered trademark of the American Medical Association.