Medical Policy: 02.04.44 

Original Effective Date: August 2013 

Reviewed: April 2021 

Revised: April 2021 

 

Notice:

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.

 

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:

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]). 

 

Clinical Context and Test Purpose

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).

 

Commercially Available Assays 

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: 

 

OncoVue 

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.

 

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 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. 

 

TruSight Cancer Sequencing Panel

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). 

 

OncoArray-500K BeadChip

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. 

 

Patients

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

 

Interventions

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.

 

Comparator

The following practice is currently being used to predict the risk of breast cancer: standard clinical risk prediction without testing for common SNVs associated with risk of breast cancer.

 

Outcomes

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.

 

Clinically Valid

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

 

Clinically Useful

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

 

Direct evidence 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.

 

Summary of Evidence

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.

 

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.

 

National Comprehensive Cancer Network (NCCN)

Genetic/Familial High-Risk Assessment: Breast, Ovarian and Pancreatic – Version 2.2021
Overview

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. 

 

Genetic Risk Assessment and Counseling

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. 

 

Breast Cancer Risk Reduction Version 1.2021

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. 

 

Regulatory Status

Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Amendments. 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.

 

Prior Approval:

Not applicable.

 

Policy:

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:

  • 02.04.74 Genetic Testing for BRCA Indications outside of Breast and Ovarian Cancers*
  • 02.04.64 Expanded Genetic Panels to Identify Cancer Risk

 

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:

 

  • BREVAGenplus
  • OncoVue Breast Cancer Risk Test
  • OncoArray
  • TruSight Cancer Sequencing Panel

 

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.

 

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
  • UpToDate. Risk Prediction Models for Breast Cancer Screening, Joann G. Elmore, M.D., MPH. Topic last updated October 28, 2014.
  • Annas GJ, Elias S. 23andMe and the FDA. N Engl J Med. Mar 13 2014;370(11):985-988. PMID 24520936
  • Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. Jul 2007;39(7):865-869. PMID 17529974
  • Easton DF, Pooley KA, Dunning AM, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. Jun 28 2007;447(7148):1087-1093. PMID 17529967
  • Hunter DJ, Kraft P, Jacobs KB, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. Jul 2007;39(7):870-874. PMID 17529973
  • Thomas G, Jacobs KB, Kraft P, et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet. May 2009;41(5):579-584. PMID 19330030
  • Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. Jun 2008;40(6):703-706. PMID 18438407
  • Gold B, Kirchhoff T, Stefanov S, et al. Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A. Mar 18 2008;105(11):4340-4345. PMID 18326623
  • Ahmed S, Thomas G, Ghoussaini M, et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet. May 2009;41(5):585-590. PMID 19330027
  • Zheng W, Long J, Gao YT, et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet. Mar 2009;41(3):324-328. PMID 19219042
  • Garcia-Closas M, Hall P, Nevanlinna H, et al. Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics. PLoS Genet. Apr 2008;4(4):e1000054. PMID 18437204
  • Beeghly-Fadiel A, Shu XO, Lu W, et al. Genetic variation in VEGF family genes and breast cancer risk: a report from the Shanghai Breast Cancer Genetics Study. Cancer Epidemiol Biomarkers Prev. Jan 2011;20(1):33-41. PMID 21119072 
  • Cai Q, Wen W, Qu S, et al. Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalize its importance in women of chinese, Japanese, and European ancestry. Cancer Res. Feb 15 2011;71(4):1344-1355. PMID 21303983
  • Han W, Woo JH, Yu JH, et al. Common genetic variants associated with breast cancer in Korean women and differential susceptibility according to intrinsic subtype. Cancer Epidemiol Biomarkers Prev. May 2011;20(5):793-798. PMID 21415360
  • Jiang Y, Han J, Liu J, et al. Risk of genome-wide association study newly identified genetic variants for breast cancer in Chinese women of Heilongjiang Province. Breast Cancer Res Treat. Jul 2011;128(1):251-257. PMID 21197568
  • Mong FY, Kuo YL, Liu CW, et al. Association of gene polymorphisms in prolactin and its receptor with breast cancer risk in Taiwanese women. Mol Biol Rep. Oct 2011;38(7):4629-4636. PMID 21125332
  • Mukherjee N, Bhattacharya N, Sinha S, et al. Association of APC and MCC polymorphisms with increased breast cancer risk in an Indian population. Int J Biol Markers. Jan-Mar 2011;26(1):43-49. PMID 21279955
  • Ota I, Sakurai A, Toyoda Y, et al. Association between breast cancer risk and the wild-type allele of human ABC transporter ABCC11. Anticancer Res. Dec 2010;30(12):5189-5194. PMID 21187511
  • Yu JC, Hsiung CN, Hsu HM, et al. Genetic variation in the genome-wide predicted estrogen response elementrelated sequences is associated with breast cancer development. Breast Cancer Res. 2011;13(1):R13. PMID 21281495
  • Dai J, Hu Z, Jiang Y, et al. Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women. Breast Cancer Res. Jan 23 2012;14(1):R17. PMID 22269215
  • Long J, Cai Q, Sung H, et al. Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet. Feb 2012;8(2):e1002532. PMID 22383897
  • Huo D, Zheng Y, Ogundiran TO, et al. Evaluation of 19 susceptibility loci of breast cancer in women of African ancestry. Carcinogenesis. Apr 2012;33(4):835-840. PMID 22357627
  • McCarthy AM, Armstrong K, Handorf E, et al. Incremental impact of breast cancer SNP panel on risk classification in a screening population of white and African American women. Breast Cancer Res Treat. Apr 2013;138(3):889-898. PMID 23474973
  • Schoeps A, Rudolph A, Seibold P, et al. Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. Genet Epidemiol. Jan 2014;38(1):84-93. PMID 24248812
  • Nickels S, Truong T, Hein R, et al. Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genet. 2013;9(3):e1003284. PMID 23544014
  • Pei J, Li F, Wang B. Single nucleotide polymorphism 6q25.1 rs2046210 and increased risk of breast cancer. Tumour Biol. Dec 2013;34(6):4073-4079. PMID 23888322
  • Wu X, Xu QQ, Guo L, et al. Quantitative assessment of the association between rs2046210 at 6q25.1 and breast cancer risk. PLoS One. 2013;8(6):e65206. PMID 23785413
  • Liu JJ, Liu JL, Zhang X, et al. A meta-analysis of the association of glutathione S-transferase P1 gene polymorphism with the susceptibility of breast cancer. Mol Biol Rep. Apr 2013;40(4):3203-3212. PMID 23334471
  • Zheng W, Zhang B, Cai Q, et al. Common genetic determinants of breast-cancer risk in East Asian women: a collaborative study of 23 637 breast cancer cases and 25 579 controls. Hum Mol Genet. Jun 15 2013;22(12):2539-2550. PMID 23535825
  • Yao S, Graham K, Shen J, et al. Genetic variants in microRNAs and breast cancer risk in African American and European American women. Breast Cancer Res Treat. Oct 2013;141(3):447-459. PMID 24062209
  • Zhou ZC, Wang J, Cai ZH, et al. Association between vitamin D receptor gene Cdx2 polymorphism and breast cancer susceptibility. Tumour Biol. Dec 2013;34(6):3437-3441. PMID 23821301
  • Chen QH, Wang QB, Zhang B. Ethnicity modifies the association between functional microRNA polymorphisms and breast cancer risk: a HuGE meta-analysis. Tumour Biol. Jan 2014;35(1):529-543. PMID 23982873
  • Xu Q, He CY, Liu JW, et al. Pre-miR-27a rs895819A/G polymorphisms in cancer: a meta-analysis. PLoS One.2013;8(6):e65208. PMID 23762318
  • Zhong S, Chen Z, Xu J, et al. Pre-mir-27a rs895819 polymorphism and cancer risk: a meta-analysis. Mol Biol Rep. Apr 2013;40(4):3181-3186. PMID 23266669
  • Fan C, Chen C, Wu D. The association between common genetic variant of microRNA-499 and cancer susceptibility: a meta-analysis. Mol Biol Rep. Apr 2013;40(4):3389-3394. PMID 23271127
  • Michailidou K, Hall P, Gonzalez-Neira A, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. Apr 2013;45(4):353-361. PMID 23535729
  • Siddiq A, Couch FJ, Chen GK, et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet. Dec 15 2012;21(24):5373-5384. PMID 22976474
  • Milne RL, Herranz J, Michailidou K, et al. A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46 450 cases and 42 461 controls from the breast cancer association consortium. Hum Mol Genet. Apr 1 2014;23(7):1934-1946. PMID 24242184
  • Joshi AD, Lindstrom S, Husing A, et al. Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium. Am J Epidemiol. Nov 15 2014;180(10):1018-1027. PMID 25255808
  • Gu C, Zhou L, Yu J. Quantitative assessment of 2q35-rs13387042 polymorphism and hormone receptor status with breast cancer risk. PLoS One. 2013;8(7):e66979. PMID 23894282
  • Gong WF, Zhong JH, Xiang BD, et al. Single Nucleotide Polymorphism 8q24 rs13281615 and Risk of Breast Cancer: Meta-Analysis of More than 100,000 Cases. PLoS One. 2013;8(4):e60108. PMID 23565189
  • Milne RL, Burwinkel B, Michailidou K, et al. Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium. Hum Mol Genet. Nov 15 2014;23(22):6096-6111. PMID 24943594
  • Lin WY, Brock IW, Connley D, et al. Associations of ATR and CHEK1 single nucleotide polymorphisms with breast cancer. PLoS One. 2013;8(7):e68578. PMID 23844225
  • Bodelon C, Malone KE, Johnson LG, et al. Common sequence variants in chemokine-related genes and risk of breast cancer in post-menopausal women. Int J Mol Epidemiol Genet. 2013;4(4):218-227. PMID 24319537
  • He XF, Wei W, Li SX, et al. Association between the COMT Val158Met polymorphism and breast cancer risk: a meta-analysis of 30,199 cases and 38,922 controls. Mol Biol Rep. Jun 2012;39(6):6811-6823. PMID 22297695
  • Dai ZJ, Shao YP, Ma XB, et al. Association of the three common SNPs of cyclooxygenase-2 gene (rs20417, rs689466, and rs5275) with the susceptibility of breast cancer: an updated meta-analysis involving 34,590 subjects. Dis Markers. 2014;2014:484729. PMID 25214704
  • Tang L, Xu J, Wei F, et al. Association of STXBP4/COX11 rs6504950 (G>A) polymorphism with breast cancer risk: evidence from 17,960 cases and 22,713 controls. Arch Med Res. Jul 2012;43(5):383-388. PMID 22863968
  • He XF, Wei W, Liu ZZ, et al. Association between the CYP1A1 T3801C polymorphism and risk of cancer: evidence from 268 case-control studies. Gene. Oct 24 2013. PMID 24513335
  • Tian Z, Li YL, Zhao L, et al. Role of CYP1A2 1F polymorphism in cancer risk: evidence from a meta-analysis of 46 case-control studies. Gene. Jul 25 2013;524(2):168-174. PMID 23628800
  • Pineda B, Garcia-Perez MA, Cano A, et al. Associations between aromatase CYP19 rs10046 polymorphism and breast cancer risk: from a case-control to a meta-analysis of 20,098 subjects. PLoS One. 2013;8(1):e53902. PMID 23342035
  • Agarwal D, Pineda S, Michailidou K, et. al. FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium. Br J Cancer. Feb 18 2014;110(4):1088-1100. PMID 24548884
  • Yu Z, Liu Q, Huang C, et al. The interleukin 10 -819C/T polymorphism and cancer risk: a HuGE review and meta-analysis of 73 studies including 15,942 cases and 22,336 controls. OMICS. Apr 2013;17(4):200-214. PMID 23574339 
  • Zhang H, Wang A, Ma H, et al. Association between insulin receptor substrate 1 Gly972Arg polymorphism and cancer risk. Tumour Biol. Oct 2013;34(5):2929-2936. PMID 23708959
  • Zheng Q, Ye J, Wu H, et al. Association between Mitogen-Activated Protein Kinase Kinase Kinase 1 Polymorphisms and Breast Cancer Susceptibility: A Meta-Analysis of 20 Case-Control Studies. PLoS One. 2014;9(3):e90771. PMID 24595411
  • Gao J, Kang AJ, Lin S, et al. Association between MDM2 rs 2279744 polymorphism and breast cancer susceptibility: a meta-analysis based on 9,788 cases and 11,195 controls. Ther Clin Risk Manag. 2014;10:269-277. PMID 24790452
  • Wang Z, Wang T, Bian J. Association between MDR1 C3435T polymorphism and risk of breast cancer. Gene. Dec 10 2013;532(1):94-99. PMID 24070710
  • Zhong S, Xu J, Li W, et al. Methionine synthase A2756G polymorphism and breast cancer risk: an up-to-date meta-analysis. Gene. Sep 25 2013;527(2):510-515. PMID 23845785
  • Saadat M. Paraoxonase 1 genetic polymorphisms and susceptibility to breast cancer: a meta-analysis. Cancer Epidemiol. Apr 2012;36(2):e101-103. PMID 22133529
  • Qin K, Wu C, Wu X. Two nonsynonymous polymorphisms (F31I and V57I) of the STK15 gene and breast cancer risk: a meta-analysis based on 5966 cases and 7609 controls. J Int Med Res. Aug 2013;41(4):956-963. PMID 23803310
  • Chen J, Yuan T, Liu M, et al. Association between TCF7L2 gene polymorphism and cancer risk: a meta-analysis. PLoS One. 2013;8(8):e71730. PMID 23951231
  • Perna L, Butterbach K, Haug U, et al. Vitamin D receptor genotype rs731236 (Taq1) and breast cancer prognosis. Cancer Epidemiol Biomarkers Prev. Mar 2013;22(3):437-442. PMID 23300018
  • Zhang K, Song L. Association between vitamin D receptor gene polymorphisms and breast cancer risk: a meta-analysis of 39 studies. PLoS One. 2014;9(4):e96125. PMID 24769568
  • Li J, Ju Y. Association between the Functional Polymorphism of Vascular Endothelial Growth Factor Gene and Breast Cancer: A Meta-Analysis. Iran J Med Sci. Jan 2015;40(1):2-12. PMID 25649829
  • He Y, Zhang Y, Jin C, et al. Impact of XRCC2 Arg188His Polymorphism on Cancer Susceptibility: A Meta-Analysis. PLoS One. 2014;9(3):e91202. PMID 24621646
  • He XF, Wei W, Su J, et al. Association between the XRCC3 polymorphisms and breast cancer risk: meta-analysis based on case-control studies. Mol Biol Rep. May 2012;39(5):5125-5134. PMID 22161248
  • McCarthy A, Keller B, Kontos D, et al. The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms. Breast Cancer Res. Jan 8 2015;17(1):1. PMID 25567532
  • Pharoah PD, Antoniou AC, Easton DF, et al. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. Jun 26 2008;358(26):2796-2803. PMID 18579814
  • Reeves GK, Travis RC, Green J, et al. Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. JAMA. Jul 28 2010;304(4):426-434. PMID 20664043
  • Braun R, Buetow K. Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data. PLoS Genet. Jun 2011;7(6):e1002101. PMID 21695280
  • Silva SN, Guerreiro D, Gomes M, et al. SNPs/pools: a methodology for the identification of relevant SNPs in breast cancer epidemiology. Oncol Rep. Feb 2012;27(2):511-516. PMID 22024983
  • Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med. Mar 18 2010;362(11):986-993. PMID 20237344
  • Devilee P, Rookus MA. A tiny step closer to personalized risk prediction for breast cancer. N Engl J Med. Mar 18 2010;362(11):1043-1045. PMID 20237351
  • Offit K. Breast cancer single-nucleotide polymorphisms: statistical significance and clinical utility. J Natl Cancer Inst. Jul 15 2009;101(14):973-975. PMID 19567420
  • Mealiffe ME, Stokowski RP, Rhees BK, et al. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. Nov 3 2010;102(21):1618-1627. PMID 20956782
  • Janssens AC, Ioannidis JP, van Duijn CM, et al. Strengthening the reporting of Genetic Risk Prediction Studies: the GRIPS statement. Genet Med. May 2011;13(5):453-456. PMID 21502867
  • Janssens AC, Ioannidis JP, Bedrosian S, et al. Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. Eur J Clin Invest. Sep 2011;41(9):1010-1035. PMID 21434890
  • Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. Feb 10 2011;364(6):524-534. PMID 21226570
  • Cummings SR, Tice JA, Bauer S, et al. Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk. J Natl Cancer Inst. Mar 18 2009;101(6):384-398. PMID 19276457
  • Jupe ER, Pugh TW, Knowlton NS. Breast cancer risk estimation using the OncoVue model compared to combined GWAS single nucleotie polymorphisms. 2009 San Antonio Breast Cancer Symposium.Abstract 3177.
  • Dite GS, Mahmoodi M, Bickerstaffe A, et al. Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. Jun 2013;139(3):887-896. PMID 23774992
  • Campa D, Kaaks R, Le Marchand L, et al. Interactions Between Genetic Variants and Breast Cancer Risk Factors in the Breast and Prostate Cancer Cohort Consortium. J Natl Cancer Inst. Jul 26 2011. PMID 21791674
  • Darabi H, Czene K, Zhao W, et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res. Feb 7 2012;14(1):R25. PMID 22314178
  • Armstrong K, Handorf EA, Chen J, et al. Breast cancer risk prediction and mammography biopsy decisions: a model-based study. Am J Prev Med. Jan 2013;44(1):15-22. PMID 23253645
  • Visvanathan K, Hurley P, Bantug E, et al. Use of Pharmacologic Interventions for Breast Cancer Risk Reduction: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol. Jul 8 2013. PMID 23835710
  • Robson M, Bradbury A, Arun Banu, et. al. American Society of Clinical Oncology Policy Statement Update: Genetic and Genomic Testing for Cancer Susceptibility. Journal of Clinical Oncology 2015.63.3628
  • Allman R, Dite GS, Hopper JL, et al. SNPs and breast cancer risk prediction for African American and Hispanic women. Breast Cancer Res Treat. Dec 2015;154(3):583-589. PMID 26589314
  • Zheng W, Wen W, Gao YT, et al. Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women. J Natl Cancer Inst. Jul 7 2010;102(13):972-981. PMID 20484103
  • Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. Feb 10 2011;364(6):524-534. PMID 21226570
  • Sakoda LC, Jorgenson E, Witte JS. Turning of COGS moves forward findings for hormonally mediated cancers. Nat Genet. Apr 2013;45(4):345-348. PMID 23535722
  • Cuzick J, Brentnall AR, Segal C, et al. Impact of a panel of 88 single nucleotide polymorphisms on the risk of breast cancer in high-risk women: results from two randomized tamoxifen prevention trials. J Clin Oncol. Mar 2017;35(7):743-750. PMID 28029312
  • National Comprehensive Cancer Network (NCCN) Breast Cancer Risk Reduction Version 1.2019.
  • National Comprehensive Cancer Network (NCCN) Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic Version 1.2020
  • UpToDate. Overview of Hereditary Breast and Ovarian Cancer Syndromes Associated with Genes other than BRCA1/2.
  • Dork, T., Park-Simon, T. W. et,.al. Recommendations Related to Genetic Testing for Breast Cancer. Jama, 323(2), 188. doi:10.1001/jama.2019
  • Infinium OncoArray-500K BeadChip.
  • Kapoor, P. M., Lindström, S., Behrens, S. et. al. Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium. Int J Epidemiol, 49(1), 216-232. doi:10.1093/ije/dyz193
  • Rudolph, A., Song, M., Brook, M. N., Milne, R. L., Mavaddat, N., Michailidou, K., Garcia-Closas, M. (2018). Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium. Int J Epidemiol, 47(2), 526-536
  • TruSight Cancer Sequencing Panel.
  • Shu X, Long J, Cai Q, et al. Identification of novel breast cancer susceptibility loci in meta-analyses conducted among Asian and European descendants. Nat Commun. Mar 05 2020; 11(1): 1217. PMID 32139696
  • Wang X, He X, Guo H, et al. Variants in the 8q24 region associated with risk of breast cancer: Systematic research synopsis and meta-analysis. Medicine (Baltimore). Feb 2020; 99(8): e19217. PMID 32080114
  • Liu H, Wei Z, Shi K, et al. Association between ABCB1 G2677T/A Polymorphism and Breast Cancer Risk: A Meta-Analysis. Crit Rev Eukaryot Gene Expr. 2019; 29(3): 243-249. PMID 31679234
  • Xu Y, Lu Z, Shen N, et al. Association of RAGE rs1800625 Polymorphism and Cancer Risk: A Meta-Analysis of 18 Case-Control Studies. Med Sci Monit. Sep 19 2019; 25: 7026-7034. PMID 31534114

 

Policy History:

  • April 2021 - Annual review, Policy revised
  • April 2020 - Annual review, Policy revised
  • April 2019 - Annual review, Policy revised
  • 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.

 

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