Abstract
Purpose
Screening with mammography and breast magnetic resonance imaging (MRI) is an important risk management strategy for individuals with inherited pathogenic variants (PVs) in genes associated with increased breast cancer risk. We describe longitudinal screening adherence in individuals who underwent cancer genetic testing as part of usual care in a vertically integrated health system.
Methods
We determined the proportion time covered (PTC) by annual mammography and breast MRI for individuals with PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, and ATM. We determined time covered by biennial mammography beginning at age 50 years for individuals who received negative results, uncertain results, or with PVs in genes without specific breast cancer screening recommendations.
Results
One hundred and forty individuals had PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, or ATM. Among these individuals, average PTC was 48% (range 0–99%) for annual screening mammography and 34% (range 0–100%) for annual breast MRI. Average PTC was highest for individuals with PVs in CHEK2 (N = 14) and lowest for individuals with PVs in TP53 (N = 3). Average PTC for biennial mammography (N = 1,027) was 49% (0–100%).
Conclusion
Longitudinal screening adherence in individuals with PVs in breast cancer associated genes, as measured by the proportion of time covered, is low; adherence to annual breast MRI falls below that of annual mammography. Additional research should examine screening behavior in individuals with PVs in breast cancer associated genes with a goal of developing interventions to improve adherence to recommended risk management.
Keywords: Breast cancer, mammography; Breast MRI, surveillance; Risk management; Longitudinal adherence; Familial and hereditary cancers
Introduction
Identification of healthy individuals with substantial lifetime breast cancer risk is one of preventive genomic medicine’s most compelling use cases [1, 2]. Knowledge of inherited cancer risk enables early breast cancer detection and prevention through enhanced screening and other risk management options. Clinical guidelines recommend considering risk-reducing surgery (i.e., prophylactic bilateral mastectomy), considering medications that lower the risk for breast cancer (i.e., chemoprevention), beginning mammography early, and using breast magnetic resonance imaging (MRI) as an adjunct to mammography [3-5]. Specific risk management recommendations differ between breast cancer-associated genes, reflecting variability in penetrance and associated lifetime breast cancer risk. For example, risk-reducing surgery is only recommended for high penetrance genes that substantially increase lifetime breast cancer risk [3].
Surveillance through routine imaging is the cornerstone of comprehensive breast cancer risk management for individuals with inherited pathogenic variants (PVs) who have not undergone risk-reducing mastectomy. The National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in Oncology recommend individuals with inherited PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2 and ATM have annual mammograms and/or breast MRI per the guidelines shown in Table 1 [3]. Screening breast MRI has improved sensitivity and cancer detection rates relative to screening mammography [6]. However, breast MRI more often leads to false-positive results that require diagnostic work-up with breast biopsy [7].
Table 1.
Pathogentic variants (PV) and National Comprenshive Cancer Network (NCCN) breast cancer (BC) surveillance recommendationsa
| Gene with PV | N = 140 | Lifetime BC risk (%) |
NCCN BC surveillance recommendations |
|---|---|---|---|
| TP53 | 3 | > 60 | Annual MRI age 20–29; Annual mammogram and MRI age 30–75 |
| BRCA1 | 58 | > 60 | Annual MRI age 25–29; Annual mammogram and MRI age 30–75 |
| BRCA2 | 51 | > 60 | Annual MRI age 25–29; Annual mammogram and MRI age 30–75 |
| PALB2 | 4 | 41–61 | Annual mammogram age 30–75; Consider annual MRI age 30–75 |
| NF1 | 1 | 15–40 | Annual mammogram age 30–50; Consider annual MRI age 30–50 |
| CHEK2 | 18 | 15–40 | Annual mammogram age 40–75; Consider annual MRI age 40–75 |
| ATM | 5 | 15–40 | Annual mammogram age 40–75; Consider annual MRI age 40–75 |
Individuals were tested between 2010 and 2018, with the majority receiving testing in later years. Study follow-up occurring through December 31, 2019. The above surveillance recommendations are from the 2019 NCCN guidelines [3]. Surveillence guidelines (age to start, modality, frequency) did not vary dramatically over the study period, with the expection of the addition of new genes for which surveillance was recommended
Adherence to recommended breast cancer screening reduces cancer morbidity and mortality and drives the cost effectiveness of cancer genetic testing [8-11]. Thus, poor adherence to screening following testing limits economic return and patient benefit [12, 13]. Unfortunately, available data suggest that patterns of mammography and breast MRI screening following genetic testing do not align with clinical guidelines [14-16]. A 2020 study reported that only 16% of women with BRCA1 and BRCA2 PVs in a national commercially insured sample had a breast MRI over the last decade [17]. Further, rates of screening breast MRI were lowest in young women with BRCA1 and BRCA2 PVs (242 per 10,000 women aged 30–39 years), despite the fact that they likely benefit the most from early imaging via this modality. Screening adherence in unaffected women with inherited PVs has also been shown to decline over time [18, 19].
Prior research has been limited by a lack of data on long-term (vs. short-term) screening adherence, particularly outside of specialty care settings and the ubiquity of approaches that consider mammography and breast MRI use in isolation. The purpose of this study is to address these limitations by describing longitudinal adherence to breast cancer screening among individuals without a personal history of breast cancer in a large integrated health system. Specifically, we report proportion of time covered (PTC) by screening modality (mammogram, breast MRI, both modalities, either modality) and genetic test results. We choose PTC to summarize long-term screening behavior because it captures delays and gaps in screening during extended periods of eligibility [20].
Materials and methods
Setting and study population
We conducted a retrospective cohort study in Kaiser Permanente Northwest (KPNW), an integrated health care delivery system serving over 600,00 members in Oregon and southwest Washington state. KPNW’s members make up about 25% of the coverage area and are representative with respect to racial and ethnic identity (69% non-Hispanic White; 8% Hispanic; 5% Asian; and 3% Black, and 15% American Indian or Alaskan Native, Pacific Islander, two or more races, or unknown race and ethnicity).
Genetic counseling services at KPNW are referral-based, including self-referral, and triaged according to perceived clinical urgency. Individuals receive pre- and post-test counseling from a genetic counselor and/or a medical geneticist. Post-test counseling includes discussion of risk management options and referrals to specialists, as needed. Appointment types during the study period include in-person and virtual (phone or telemedicine/video).
Eligible individuals for this study were 18 years or older at the time of genetic testing and had at least one genetic test performed between January 1, 2010 and December 31, 2018 that included one or more high penetrance hereditary cancer susceptibility gene (BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, EPCAM). We excluded health plan members who had actively opted out of research participation, generally, or genetic research, specifically. We used the KPNW tumor registry to exclude individuals with prior diagnoses of breast cancer. Finally, to increase the likelihood that our sample included individuals with breasts, we excluded health system members with male, other, or unknown legal sex as recorded in the electronic health record (EHR). The study was approved by the KPNW Institutional Review Board with a waiver of informed consent.
Data resources
KPNW maintains comprehensive administrative and clinical databases that are available for research. Data from the EHR, administrative systems, and claims are incorporated into a research data warehouse using a unique health record number for each health plan member [21, 22].
Genetic testing data
Information about genetic test orders and results was obtained from a clinical database that tracks orders and results for genetic tests sent to commercial laboratories at the gene and variant level. We collapsed order data at the member level and used the earliest order as the index order except when results from the subsequent order changed the patient’s clinical management. For index orders, we used information contained in the order itself to determine test type as divided into the following predetermined categories: single site; gene rearrangement; panel ≤ 50 genes; panel > 50 genes. Per database design, genetic test results were categorized into the following predetermined categories: PV(s) identified; negative; or only variants of uncertain significance (VUS) identified. Negative results included benign and likely benign variants identified.
Imaging data
We searched the research data warehouse for evidence of mammograms and breast MRIs using procedure codes compiled from the Healthcare Effectiveness Data and Information Set performance measures, prior literature, and manual review of code lists. We captured imaging in the two years prior to the index genetic test and during study follow-up, defined as the time between the index genetic test and either the end of the study period (December 31, 2019) or one of the following censoring events: an incident cancer diagnosis, bilateral mastectomy, disenrollment from the health plan, reaching age 75 years, or death. Because current coding does not distinguish screening from diagnostic imaging, all breast MRI procedures were treated as screening tests.
Additional member data
Age, race/ethnicity, and insurance type (commercial or private, Medicare, Medicaid, other) were extracted from the research data warehouse at the index test order date. Incident cancer diagnoses were determined using the tumor registry data. We ascertained disenrollment or death from health plan membership data.
Statistical analysis
We summarized cohort characteristics including years of health plan enrollment before genetic testing and years of study follow-up (total days elapsed between genetic testing and either the end of the study period or a censoring event).
We defined screening/surveillance adherence as the cumulative PTC by recommended imaging following methods described by Chubak et al. [20] Among individuals with NCCN surveillance recommendations per Table 1, we calculated the number of days they were eligible for MRI, alone, or both MRI and mammography, based on their date of genetic testing and age, which served as the denominator for calculating PTC. Following NCCN guidelines, we defined the number of days in compliance (the covered time) by giving 1 year of screening coverage from the date of each mammogram or breast MRI. We calculated time covered by each type of imaging, independently, the time covered by either type of imaging, and the time covered by both types of imaging.
Among individuals without NCCN breast cancer surveillance recommendations (i.e., individuals who tested negative, had variants of uncertain significance (VUS) identified, or had other non-breast cancer related PV identified), we calculated the number of days they were eligible for routine biennial mammography following National Committee for Quality Assurance (NCQA) recommendations. We defined the number of days in compliance (time covered) by giving two years of screening coverage from the date of each mammogram, starting the date they received genetic testing or reached 50 years old [23].
In all cases, individuals received credit in the form of covered days for imaging procedures they received in the year (or two years in the case of those without NCCN recommendations) prior to genetic testing. We calculated average PTC, overall and by genetic test results, including stratified by breast cancer-associated gene, for individuals with any amount of eligible denominator time and for individuals who had at least six months of eligible denominator time in which to observe imaging events.
Results
The sample comprised 1,167 members after exclusions (Fig. 1), of which 140 had PVs in genes with NCCN breast cancer surveillance recommendations (Table 1). Those with NCCN recommendations differed from the rest of the sample with respect to their length of study follow-up (mean 2.6 years vs. 3.1 years, p < 0.01); age at genetic testing (mean 43.4 years vs. 49.7 years, p < 0.0001); and type of genetic testing received (mostly single gene tests vs. small panel tests, p < 0.001). The two groups were otherwise similar with respect to race/ethnicity (mostly non-Hispanic White), insurance type (mostly commercially or privately insured), years of prior health plan enrollment (mean 10.1 years), and year of genetic testing (mostly tested later in the study period). Among the 1,027 members who did not have NCCN breast cancer surveillance recommendations, 812 (79.1%) tested negative, 165 (16.1%) had only VUS identified through testing, and 50 (4.9%) had PVs in genes without breast cancer surveillance recommendations (Table 1).
Fig. 1.
Cohort construction
Eligibility and proportion of time covered in those with NCCN breast cancer surveillance recommendations
Sixteen percent (n = 22) of the 140 members eligible for annual mammography per NCCN guidelines never reached the recommended screening age during the study period or had a censoring event prior to reaching that age. Similarly, eight percent (n = 11) never reached the age recommended to begin annual breast MRI or had a censoring event prior to reaching that age (Table 2). Among individuals with any amount of eligible denominator time, the average PTC by annual screening mammography was 48% (range 0–100%, N = 118) and by annual breast MRI was 34% (range 0–100%, N = 129). When considering annual mammography and annual MRI in combination, the average PTC by either modality was 63% (range 0–100%, N = 118), and the average PTC by both modalities was 19% (range 0–99%, N = 118). Average PTC varied by gene and increased slightly when individuals with less than six months of eligible denominator time were excluded from calculations (Table 4).
Table 2.
Patient characteristics by National Comprenshive Cancer Network (NCCN) breast cancer (BC) surveillance recommendations
| Characteristic | Total N = 1167 |
NCCN recsa N = 140 |
All others N = 1,027 |
P valueb |
|---|---|---|---|---|
| Study follow-up (years) | ||||
| Mean (range) | 3.0 (< 1–9.9) | 2.6 (< 1–9.9) | 3.1 (< 1–9.9) | 0.01 |
| Age at index test (years) | ||||
| Mean (range) | 49.0 (18–74) | 43.4 (18–73) | 49.7 (18–74) | < 0.0001 |
| Race/ethnicity, N (%) | ||||
| Non-Hispanic White | 1017 (87.2) | 128 (91.4) | 889 (86.6) | 0.32 |
| Hispanic | 46 (3.9) | 2 (1.4) | 44 (4.3) | |
| Black | 15 (1.3) | 2 (1.4) | 13 (1.3) | |
| Asian | 38 (3.3) | 5 (3.6) | 33 (3.2) | |
| All other groupsc | 20 (1.7) | 0 (0.0) | 20 (2.0) | |
| Race/ethnicity unknown | 31 (2.7) | 3 (2.1) | 28 (2.7) | |
| Insurance type, N (%) | ||||
| Commercial or private | 959 (82.2) | 122 (87.1) | 837 (81.5) | 0.41 |
| Medicare | 194 (16.6) | 17 (12.1) | 177 (17.2) | |
| Medicaid | 11 (0.9) | 1 (0.7) | 10 (1.0) | |
| Other | 3 (0.3) | 0 (0.0) | 3 (0.3) | |
| Prior health plan enrollment (years) | ||||
| Mean (range) | 10.1 (< 1–51.9) | 8.8 (< 1–49.0) | 10.1 (< 1–51.9) | 0.17 |
| Index test year, N (%) | ||||
| 2010 | 67 (5.7) | 16 (11.4) | 51 (5.0) | 0.06 |
| 2011 | 79 (6.8) | 11 (7.9) | 68 (6.6) | |
| 2012 | 76 (6.5) | 10 (7.1) | 66 (6.4) | |
| 2013 | 100 (8.6) | 12 (8.6) | 88 (8.6) | |
| 2014 | 99 (8.5) | 15 (10.7) | 84 (8.2) | |
| 2015 | 138 (11.8) | 16 (11.4) | 122 (11.9) | |
| 2016 | 201 (17.2) | 24 (17.1) | 177 (17.2) | |
| 2017 | 202 (17.3) | 15 (10.7) | 187 (18.2) | |
| 2018 | 205 (17.6) | 21 (15.0) | 184 (17.9) | |
| Index test classification, N (%) | ||||
| Single gene test | 227 (19.5) | 77 (55.0) | 150 (14.6) | < 0.0001 |
| Rearrangement (BART) | 37 (3.2) | 4 (2.9) | 33 (3.2) | |
| Small panel (≤ 50 genes) | 861 (73.8) | 55 (39.3) | 806 (78.5) | |
| Large panel (> 50 genes) | 42 (3.6) | 4 (2.9) | 38 (3.7) | |
| Index test result, N (%) | ||||
| Negative (negative) | 812 (69.6) | NA | 812 (79.1) | |
| VUS only (inconclusive) | 165 (14.1) | NA | 165 (16.1) | |
| PV variant(s) | 190 (16.3) | 140 (100.0) | 50 (4.9)d |
BART BRACnalysis Rearrangement Test, VUS variant of uncertain significance, PV pathogenic variants
Pathogenic variant (PV) in BRCA1, BRCA2, CHEK2, ATM, PALB2, TP53, NFI
P value for comparing those with NCCN recommendations to all others
Identified in medical record as Native American, Native Hawaiian or Pacific Islander, more than one race/ethnicity, or other (no further description given)
Pathogenic variant (PV) APC, MSH6, MUTYH, MSH2, BRIP1, MLH1, RAD51C, FLCN, RAD51D, PMS2, EPCAM, RAD50, BARD1, FANCC, SMAD4, SMARCA4, TSC1, DICER1
Table 4.
Average proportion time covered (PTC) among those with National Comprenshive Cancer Network (NCCN) breast cancer (BC) surveillance recommendations, by imaging type and gene with pathogenic variant (PV)
| Any eligible follow- up |
≥ 6 months follow-up | |||
|---|---|---|---|---|
|
|
|
|||
| N | % (range) | N | % (range) | |
| Annual MRI | 129 | 34.4 (0–100.0) | 105 | 36.4 (0–100.0) |
| TP53 | 3 | 6.4 (0–19.1) | 3 | 6.4 (0–19.1) |
| BRCA1 | 55 | 34.7 (0–95.6) | 42 | 35.4 (0–95.6) |
| BRCA2 | 48 | 31.9 (0–91.0) | 40 | 35.4 (0–91.0) |
| PALB2 | 4 | 33.0 (0–85.0) | 4 | 33.0 (0–85.0) |
| NF1 | 1 | 0 (NA) | 1 | 0 (NA) |
| CHEK2 | 14 | 53.6 (0–100.0) | 12 | 55.8 (0–100.0) |
| ATM | 4 | 25.6 (0–62.8) | 3 | 34.1 (0–62.8) |
| Annual Mammogram | 118 | 47.6 (0–99.6) | 95 | 54.6 (0–99.6) |
| TP53 | 3 | 19.0 (0–57.0) | 3 | 19.0 (0–57.0) |
| BRCA1 | 49 | 47.1 (0–99.6) | 37 | 52.5 (0–99.6) |
| BRCA2 | 43 | 47.9 (0–97.4) | 35 | 56.9 (0–97.4) |
| PALB2 | 4 | 53.5 (0–88.1) | 4 | 53.5 (0–88.1) |
| NF1 | 1 | 40.1 (NA) | 1 | 40.1 (NA) |
| CHEK2 | 14 | 53.2 (0–99.0) | 3 | 62.1 (0–99.0) |
| ATM | 4 | 49.9 (0–84.5) | 3 | 66.6 (0–84.5) |
| Either imaging type | 118 | 63.2 (0–100.0) | 95 | 68.5 (0–100.0) |
| TP53 | 3 | 25.4 (0–57.0) | 3 | 25.4 (0–57.0) |
| BRCA1 | 49 | 72.9 (0–99.6) | 37 | 66.1 (0–99.6) |
| BRCA2 | 43 | 61.8 (0–98.9) | 35 | 70.7 (5.9–98.9) |
| PALB2 | 4 | 65.1 (0–90.5) | 4 | 65.1 (0–90.5) |
| NF1 | 1 | 40.1 (NA) | 1 | 40.1 (NA) |
| CHEK2 | 14 | 74.6 (0–100.0) | 3 | 80.1 (35.4–100.0) |
| ATM | 4 | 61.6 (0–95.9) | 3 | 82.2 (60.3–95.9) |
| Both imaging types | 118 | 19.0 (0–99.0) | 95 | 22.6 (0–99.0) |
| TP53 | 3 | 0 (NA) | 3 | 0 (NA) |
| BRCA1 | 49 | 18.1 (0–87.6) | 37 | 21.4 (0–87.6) |
| BRCA2 | 43 | 17.7 (0–84.0) | 35 | 21.7 (0–84.0) |
| PALB2 | 4 | 21.4 (0–58.4) | 4 | 21.4 (0–58.4) |
| NF1 | 1 | 0 (NA) | 1 | 0 (NA) |
| CHEK2 | 14 | 32.2 (0–99.0) | 3 | 37.6 (0–99.0) |
| ATM | 4 | 13.9 (0–51.5) | 3 | 18.5 (4.1–51.5) |
Eligibility and proportion time covered in those without NCCN breast cancer surveillance recommendations
Forty one percent (n = 419) of the 1,027 members without NCCN surveillance recommendations never reached age 50 years or had a censoring event prior to reaching age 50 years during the study period (Table 3). Among those with any amount of eligible denominator time (N = 608), the average PTC by biennial mammography was 49% (0–100%). Average PTC varied based on genetic test results (negative, VUS only, or other PV identified) and increased slightly when individuals with less than six months of eligible denominator time were excluded from calculations (Table 5).
Table 3.
Imaging eligibility and use by National Comprenshive Cancer Network (NCCN) breast cancer (BC) surveillance recommendations
| Total N (%) |
NCCN recsa N (%) |
All othersb N (%) |
|
|---|---|---|---|
| Mammogram | 1167 (100) | 140 (100) | 1027 (100) |
| Never eligible | 441 (38) | 22 (16) | 419 (41) |
| Incident breast cancer diagnosis | 5 | 2 | 3 |
| Bilateral mastectomy | 7 | 3 | 4 |
| Disenrolled from health system | 155 | 10 | 145 |
| Death | 12 | 0 | 12 |
| Never reached eligible age | 262 | 7 | 255 |
| Eligible, but never had mammogram | 197 (17) | 29 (21) | 168 (16) |
| Eligible and had ≥ 1 mammogram | 529 (45) | 89 (64) | 440 (43) |
| MRI | – | 140 (100) | – |
| Never eligible | – | 11 (8) | – |
| Incident breast cancer diagnosis | – | 1 | – |
| Bilateral mastectomy | – | 1 | – |
| Disenrolled from health system | – | 4 | – |
| Never reached eligible age | – | 5 | – |
| Eligible, but never had MRI | – | 42 (30) | – |
| Eligible and had ≥ 1 MRI | – | 87 (62) | – |
Pathogenic variant (PV) in BRCA1, BRCA2, CHEK2, ATM, PALB2, TP53, NFI
12% (8/68) of those with VUS in BRCA1, BRCA2, CHEK2, ATM, PALB2, TP53, NFI had ≥ 1 MRI during study follow-up
Table 5.
Average proportion time covered (PTC) among those without National Comprenshive Cancer Network (NCCN) breast cancer (BC) surveillance recommendations, by genomic test result
| Any eligible follow-up |
At least 6 months follow-up |
|||
|---|---|---|---|---|
| N | % (range) | N | % (range) | |
| Biennial mammogram | 608 | 48.9 (0–100.0) | 557 | 51.4 (0–100.0) |
| Negative | 475 | 50.3 (0–100.0) | 432 | 53.0 (0–100.0) |
| VUS | 104 | 44.4 (0–100.0) | 98 | 46.1 (0–100.0) |
| Other pathogenic variant (PV)a | 29 | 40.6 (0–100.0) | 27 | 43.5 (0–100.0) |
PV in APC, MSH6, MUTYH, MSH2, BRIP1, MLH1, RAD51C, FLCN, RAD51D, PMS2, EPCAM, RAD50, BARD1, FANCC, SMAD4, SMARCA4, TSC1, DICER1
Discussion
Our study reports PTC as a measure of longitudinal adherence to screening mammography and breast MRI following cancer genetic testing in an integrated health system. Overall, our study findings indicate that individuals with breast cancer PVs identified in this setting were subsequently covered by either recommended breast MRI or mammography imaging over 60% of the time they were eligible for screening. However, individuals were rarely covered by both screening modalities at the same time. There was also variation in longitudinal adherence between women with PVs in the same breast cancer-associated gene (see ranges in Table 4) and across women grouped by breast cancer-associated gene (see averages in Table 4), which warrant study in larger samples.
Within this integrated health care system, individuals with NCCN recommendations (PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, ATM) were covered by an annual screening mammogram for about half of the time that they were eligible. This is similar to the proportion of time that individuals without NCCN recommendations (all others) were covered by biennial screening mammography per NCQA recommendations. Prior studies report longitudinal mammography adherence rates in average-risk, insured women ranging from 42 to 85%, depending on population and length of follow-up [24-27]. Thus, while our results show room to improve mammography adherence in individuals with PVs in breast cancer-associated genes, they highlight the need to focus particular attention on increasing adherence to recommended screening MRI. To this point, individuals with inherited PVs in TP53, BRCA1, and BRCA2, who have lifetime breast cancer risk > 60% and are recommended to begin annual MRIs at age 30, were covered between 6 and 35% of the time they were eligible in this setting.
Additional research is needed to better understand the factors leading to poor adherence to recommended MRI screening. Barriers reported in prior studies include claustrophobia, time constraints, financial concerns, beliefs that physicians will not provide necessary referrals or will not believe that MRI is indicated, and patients’ lack of interest [17, 28]. There is a financial burden posed by screening MRI, even in insured populations such as this one, as the protections provision of the Affordable Care Act do not apply to screening MRI. A recent paper showed that between 2009 and 2017 women had a mean out of pocket cost of $282 per screening MRI and that this increased to $443 when considering women with any (verses no) cost-share [29]. Cost barriers to screening MRI would likely be even more pronounced in health care setting serving uninsured or under-insured individuals. Finally, there is lack of consensus about how to manage breast cancer screening during pregnancy and lactation, contributing to substantial screening lapses [30]. As individuals with inherited PVs in breast cancer-associated genes are recommended to begin screening during childbearing years, there is a clear need to establish best practices in this area.
There are few evidence-based interventions targeting barriers to breast cancer screening adherence in individuals with inherited PVs, specifically. A recent review by Mittendorf et al. summarized approaches to improve adherence to risk management among individuals with hereditary cancer syndromes [31]. They report increasing access to multidisciplinary high-risk clinics as “one-stop-shops” where patients can visit multiple specialists and undergo multiple surveillance procedures is one promising approach. In addition to reducing logistical burden, high-risk clinics can act as a “medical home,” providing much needed infrastructure, including care reminders, access to navigators and social workers, and other resources [32, 33]. However, establishing high-risk clinics is expensive and time-consuming and will not be feasible in many contexts [34]. Possible alternatives for remote and low-resource settings could include mobile screening units and technology-based solutions like automated screening reminders [35]. Developing explicit screening quality measures for individuals with inherited PVs, similar to the NCQA metrics, is another way to encourage (or mandate) health systems increase the inclusiveness of their breast cancer screening programs. Legislative efforts that removed out of pocket costs for screening MRI for high-risk women in Connecticut, New York, and Illinois are also examples of policy interventions that may increase breast MRI uptake [29].
Understanding screening behavior among individuals who test negative or receive uncertain genetic test results is a question of interest to the genomics and cancer prevention communities [14, 36]. One concern is that individuals who test negative will be falsely reassured about their cancer risk and will stop screening all together, as opposed to following recommendations for the general population. In this sample, individuals who tested negative, had VUS identified, or had other non-breast cancer related PV identified were each covered by biennial mammography for about half of the time they were eligible. While longitudinal adherence could be improved in these groups, our findings do not suggest widespread post-testing mammography cessation. It is also unclear if eligible individuals who never received a mammogram following testing stopped screening after learning their test results or never participated to begin with. A second concern is that individuals who receive uncertain test results will pursue screening that is not clinically indicated, particularly screening MRI. We found that 12% (8/68) of those with VUS in BRCA1, BRCA2, CHEK2, ATM, PALB2, TP53, or NFI had one or more MRIs after testing, verses 62% (87/140) of those with PV in these genes. Individuals with VUS in our sample may also have been eligible for screening MRI given their lifetime cancer risk. Thus, our findings align with those of recent study suggesting that overtreatment of individuals with VUS in breast cancer-associated genes is uncommon [14].
Our results are some of the first to document long-term screening adherence among insured individuals with inherited breast cancer PVs and no personal history of breast cancer. We evaluated a large sample of individuals who received genetic testing as part of usual care in an integrated health system with high quality capture of imaging procedures. However, our study has limitations that are important to note. First, given the small number of individuals with a given PV, our observations may not be generalizable, particularly for those with PVs in TP53, PALB2, NF1, CHEK2, and ATM. Studying individuals with relatively rare genetic risk factors will require broader collaboration across settings to increase sample sizes, improve generalizability, and identify health disparities. To this point, our sample was highly homogenous with respect to race/ethnicity and insurance status. Second, average PTC is sensitive to total observation time, meaning individuals contribute equally to average PTC calculations, regardless of how long they were followed [20]. To help address this limitation, we calculated average PTC in individuals with ≥ 6 months follow-up, to contextualize the impact of total observation time on PTC estimates. Third, we defined each individual’s breast cancer screening recommendations based on age and genetic test result, alone, though family history of cancer and other clinical factors also impact screening recommendations. A more nuanced approach would have required chart abstracting individual screening recommendations, which was beyond our scope. Finally, our decision to exclude health system members with male, other, or unknown sex as recorded in the EHR limits our findings’ generalizability to individuals with breasts who should be receiving breast cancer surveillance due to inherited PVs, but whose legal sex in the EHR was not female (for example, transgender men who have had a legal sex change).
Conclusion
We found significant gaps in adherence to recommended breast cancer surveillance in individuals with PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, and ATM, with particularly low adherence to recommended screening MRI in individuals with PVs in TP53, BRCA1, and BRCA2. Our findings highlight the need for research to improve screening adherence across a variety of clinical settings, particularly as recommendations for annual screening MRI expand to individuals with PVs conferring moderate to high breast cancer risk (e.g., ATM, CHEK2, PABL2) [37].
Acknowledgements
This work was supported by the Clinical Sequencing Evidence-Generating Research (CSER) consortium funded by the National Human Genome Research Institute with co-funding from the National Institute on Minority Health and Health Disparities (NIMHD) and the National Cancer Institute (NCI). The CSER consortium represents a diverse collection of projects investigating the application of genome-scale sequencing in different clinical settings including pediatric and adult subspecialties, germline diagnostic testing and tumor sequencing, and specialty and primary care.
Funding
This work was primarily supported by the National Institutes of Health: U01HG007292 (MPIs: Wilfond, Goddard), U24HG007307 (PI: G. Jarvik); K08HG010488 (PI: S. Knerr).
Footnotes
Conflict of interest The authors have no conflicts of interest outside the grant funding listed in the funding section.
Ethical approval The study was approved by the Kaiser Permanente Northwest Institutional Review Board with a waiver of informed consent.
Data Availability
Enquiries about data availability should be directed to the authors.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Enquiries about data availability should be directed to the authors.