A pilot study to increase cascade genetic testing in families with hereditary cancer syndromes

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Background: There is great need to build and evaluate tools and strategies to improve cascade genetic risk evaluation in families at high risk for hereditary cancer. The Genetic Information and Family Testing (GIFT) Trial (CA254822) is a population-based intervention that examines features of a virtual platform that provides genetic risk education (GRE) and low-cost genetic testing (GT) to relatives of adult patients diagnosed with cancer in 2018-19 in Georgia and California and tested positive for a clinically relevant germline pathogenic variant (PV). We present findings of a pilot study intended to inform the GIFT Trial protocol and platform features. Methods: We surveyed 277 women diagnosed with breast cancer in 2017, reported to the Georgia SEER registry, and received genetic testing (95% of whom had a clinically relevant PV). We then invited respondent patients to enroll in the intervention phase which provided online GRE, human pretest genetic navigator support, and an offer of low-cost GT through Color Health, Inc. to all untested 1st or 2nd degree relatives. Respondent patients were eligible for the intervention if they reported a PV on genetic testing and had at least one relative who had not received GT. Enrolled patients invited relatives through the platform by providing email addresses. Family clusters were block randomized to free vs $50 test costs at the time of the initial patient invitation. Results: At study midpoint, 117 of 277 patients (42%) had returned surveys: median age was 51 and 22% were African American. The most frequent PVs reported by the patients were BRCA1/2 (41%), CHEK2 (21%), and PALB2 (8%). Half (54%) had previously encouraged all of their brothers to get GT and 71% had encouraged all of their sisters to get GT. Three-quarters (78%) strongly agreed it was important for relatives to understand their genetic risk for cancer, and half (54%) strongly agreed they would like to make it easier for relatives to get genetic testing. The median number of patient-reported untested relatives in a family was 8.5 (25th-75th percentile: 4-14). Most respondent patients were eligible for the intervention phase (N = 108, 93%). About one-quarter had enrolled in the intervention at midpoint (16 of 53 in no-cost arm vs 16 of 55 in $50 arm). Patients in the no-cost arm invited 21 relatives, 10 of whom had enrolled with 8 ordering GT (38% of invited relatives). Patients in the $50 arm invited 38 relatives, 18 of whom had enrolled with 17 ordering GT (45% of invited relatives). Overall, about half of enrolled relatives (46%) were men. Conclusions: Breast cancer patients with PVs make substantial efforts to communicate with family members about genetic risk; but they strongly endorse the need for additional support to facilitate this complex communication. Interim pilot findings suggest that a low-cost online navigator-supported intervention can directly engage relatives with little difference in GT uptake by test cost arms.

Ancestry-specific risk of triple-negative breast cancer (TNBC) associated with germline pathogenic variants (PV) in hereditary cancer (CA) predisposition genes

10517

Background: TNBC represents ̃15% of invasive BC. Ancestry-specific variabilities in TNBC risk are well-described, with African American (AA) women experiencing higher incidence and mortality from TNBC than women of other races/ethnicities. Increased risk of TNBC has been associated with both rarer (e.g. RAD51C/D, BARD1) and more commonly detected (e.g. BRCA1/2, PALB2) germline PV in hereditary CA predisposition genes, but less is known about ancestry-specific TNBC risks for PV carriers. Methods: We examined clinical and genetic records from women referred for multigene CA panel testing (9/2013-5/2020). Multivariable logistic regression was used to test associations of PV in 13 genes with risk of TNBC after accounting for age, ancestry, and personal/family CA history. We analyzed each gene in the full cohort, and in subcohorts defined by self-reported ancestry. Effect sizes are expressed as odds ratios with 95% confidence intervals. Seven genes are not reported in ancestry-stratified analyses due to small numbers of PV carriers with TNBC. Results: From 627,219 individuals referred for multigene panel testing, 115,337 (18.4%) women with personal history of BC were identified, of whom 17,951 (15.6%) reported TNBC. Personal history of TNBC was reported more frequently in women of African ancestry (26.9%) than in women of European (13.9%) or Asian (11.7%) or Latinx ancestry (14.9%). Ancestry-stratified risks of TNBC associated with germline PVs are seen in the Table. Conclusions: While small samples sizes limit some gene-specific analyses, comparable ancestry-specific risks of TNBC were seen across the racial/ethnic groups examined here. [Table: see text]

Simulation modeling as a tool to support clinical guidelines and care for breast cancer prevention and early detection in high-risk women

10525

Background: To evaluate the incremental short- and long-term benefits and harms of primary prevention with risk reducing medication in high-risk women receiving screening mammography. Methods: We adapted an established, validated Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer discrete event microsimulation model developed to synthesize data the impact of using risk-reducing medication and annual mammography among women with a 3% or higher five-year risk of developing breast cancer. We also examined the effects of supplemental MRI. The model follows a simulated cohort of millions of US women from birth to death. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, breast cancer risk, incidence and stage, screening performance, survival by age, stage, and subtype, treatment efficacy, and other cause mortality. Breast cancer risk was modeled based on family history, breast density, age and history of past breast biopsy. We compared two strategies, annual 3D mammography alone vs. annual 3D mammography and a 5-year course of risk reducing medication at various starting ages, and adding MRI to each approach. Outcomes included the benefits of risk-reducing drugs (avoiding breast cancer) and screening (stage, breast cancer death). Harms included drug side effects and screening false positives and overdiagnosis. Sensitivity analysis tested the impact of uncertainty in model inputs and assumptions on results. Results: Compared to mammography alone, adding risk reducing medication could decrease invasive breast cancer incidence by 30%, and breast cancer deaths by 30% (Table). However, due to reduction in breast cancer incidence, risk reducing medication could result in a 3% increase in false positive results; adding MRI increases benefits but also increases false-positive results. Benefits and harms of risk reducing medication and breast cancer screening strategies for women at high-risk of developing breast cancer. Conclusions: Risk-reducing mediation reduces the risk of hormone-receptor positive breast cancer, and combining this with mammography (and/or MRI) improves earlier detection. Additional work is ongoing to incorporate adverse effects of therapy. Simulation modeling can be used to provide individualized data to facilitate discussions about breast cancer prevention and early detection among high-risk women seen in clinical practice.[Table: see text]

National claims data analysis of outcomes of hospitalized cancer patients without COVID-19 infection during versus prior to the COVID-19 pandemic

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Background: There has been growing concern regarding the impact of the COVID-19 pandemic on health care delivery and disruption of care to cancer patients. Reductions in cancer surgeries, delays in administration of life saving chemo and radiation therapies, and lower rates of cancer-related hospitalizations have been reported. While cancer patients with COVID-19 infection have poor hospitalization outcomes, less is known about the outcomes of hospitalized cancer patients without the infection. This study aimed to describe the impact of the COVID-19 pandemic on outcomes of the most common cancer-related hospital admissions for patients without COVID-19 infection at a national level using insurance claims. Given the concern for disruptions in their care, we hypothesized that hospitalized cancer patients may have worse outcomes. Methods: We used the Optum Clinformatics Data Mart, consisting of claims records linked to electronic health records, including an average of 8 million adult Americans per year enrolled for at least 6 months. We identified cancer-related hospitalizations from 02/2018-05/2021 and included patients with at least of one of these cancer types: breast, prostate, bladder, ovarian, cervical, lung, colorectal, esophageal, liver, small intestine, gastric, or gallbladder cancer. Patients with cancer-related hospitalization who had COVID-19 infection were excluded. The main outcome was “severe adverse outcome” and included at least one of the following: mortality during or within 30 days of hospitalization, mechanical ventilation during hospitalization, intensive care unit admission, or discharge to hospice. We used Poisson regression to compare the number of hospitalizations before (2/1/2018-1/30/2020) and during (2/1/2020-5/30/2021) the pandemic and a Chi-squared test to compare the proportion of cancer-related hospitalizations with severe adverse outcomes over that time period in 4-month intervals and across cancer types, gender, (male vs female) and geography (the 9 Census Bureau regions). Results: There were 82,796 cancer-related hospitalizations in the period 2/2018-05/2021. A slightly higher proportion of cancer-related hospitalizations resulted in a severe adverse outcome during the pandemic as compared to prior to the pandemic (41.8% vs 40.9%; p = 0.012). There were no differences by cancer site, gender, or geography. The number of hospitalizations was lower during vs prior to the pandemic (p < 0.0001). Conclusions: The number of cancer-related hospitalizations during the pandemic was lower compared to before the, and a slightly higher proportion of those hospitalized experienced severe adverse outcomes among insured U.S. cancer patients without COVID-19 infection. The lower number of cancer-related hospitalizations during the pandemic warrants further investigation.

Harnessing artificial intelligence to automate delineation of volumetric breast cancers from magnetic resonance imaging to improve tumor characterization

597

Background: Automated breast tumor identification and segmentation in magnetic resonance imaging (MRI) is a difficult and crucial area of study in breast cancer research. Artificial intelligence (AI) models are increasingly being developed for automated localization of lesions in imaging studies to facilitate quantitative assessment of features for improved diagnostic, prognostic and predictive performance. Such models have had success in detecting breast cancers in mammography, ultrasound and CT, but few have achieved three-dimensional (3D) volumetric tumor segmentation from breast MRI. The purpose of this study was to apply two state-of-the-art AI – specifically, deep learning (DL) - algorithms to 3D MRI breast cancer data and identify the higher performing algorithm for precise segmentation of breast tumors. Methods: We evaluated pre-treatment, T1 post-gadolinium contrast enhanced breast MRI from 222 patients with known breast cancers (n = 262). Images were split into training (n = 142), validation (n = 36), and hold-out test (n = 44) datasets. Two DL algorithms, U-Net and VAE-UNet, were trained to classify tumors on the training dataset across 1000 epochs. The output for each is a precise localization and segmentation of each tumor at the pixel level from every MRI image. We evaluated the performance of each algorithm using 5-fold cross-validation and testing on the validation and test sets. We calculated a dice accuracy score for each model as the performance comparison metric. Results: The highest dice accuracy score achieved on the validation dataset by generic U-Net was 83.38%, with an average across 1000 epochs of 62.41%. The highest dice accuracy achieved on the validation dataset by VAE-UNet was 82.62%, with an average across epochs of 61.28%. On our test dataset, the highest dice accuracy score achieved by U-Net was 93.09%, with an average across epochs of 66.31%, and the highest accuracy score for VAE-UNet was 90.98%, with average across epochs of 50.47%. Although U-Net appeared to perform slightly better than VAE-Unet for most cases, there were distinct cases where VAE-UNet outperformed U-Net (dice score up to 59% better than U-Net). Subsequent analysis indicated that VAE-UNet preferentially outperforms U-Net for tumors with low sphericity (p = 0.001). Conclusions: Our results suggest that U-Net is well suited for segmenting breast tumors from breast MRI in most cases, but that VAE-UNet outperforms U-Net when the tumor shapes are less spherical. Our findings could inform the choice of DL algorithms in research and clinical endeavors that rely on accurate breast cancer tumor segmentation. In particular, these two tools could be configured to facilitate tumor assessment from breast MRI in the clinical setting for: breast cancer screening in high-risk patient populations, pre-surgical planning, and monitoring of treatment response.

Association of germline genetic testing results with chemotherapy regimens received by women with early-stage breast cancer

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Background: Germline genetic testing is widespread after breast cancer diagnosis and increasingly informs treatment decisions; however, guidelines do not advise selecting chemotherapy regimens based on genetic testing results. It is unknown whether women with pathogenic variants (PVs) in BRCA1, BRCA2 ( BRCA1/2) or other cancer risk genes receive different chemotherapy regimens than women with negative genetic testing results. Methods: We linked Surveillance, Epidemiology and End Results (SEER) registry records from Georgia and California to clinical germline genetic testing results from four participating laboratories (Ambry, Bioreference/GeneDx, Invitae, and Myriad). For this analysis, we included patients who: 1) were diagnosed with stages I-III breast cancer, either hormone receptor-positive and HER2-negative (HR+HER2-) or triple-negative, in Georgia or California from 2013-2017; 2) received chemotherapy based on SEER records; and 3) linked to a genetic testing result. We further selected cases by genetic testing results: 50% PVs in BRCA1/2 or another cancer risk gene, 25% variant of uncertain significance (VUS) only and 25% negative. We extracted details of chemotherapy regimens from SEER text fields completed by registrars. We categorized regimens by drug classes reported (anthracycline, taxane, platinum, nitrogen mustard, other). We used multivariable models that controlled for age, race/ethnicity, stage, grade, surgical procedure, radiotherapy receipt and geographic site to test whether PV carriers received a more intensive chemotherapy regimen. For HR+HER2-, a more intensive regimen was defined as at least three drugs including an anthracycline and for triple-negative, as at least four drugs including an anthracycline and a platinum (versus fewer drugs). Results: 2,293 women were included, 1,451 with HR+HER2- and 842 with triple-negative disease. On multivariable analysis, receipt of a more intensive chemotherapy regimen was associated with having a BRCA1/2 PV among women with HR+HER2- disease (odds ratio 1.22, p = 0.036), but not among women with triple-negative disease. Moreover, platinum use was elevated in BRCA1/2 PV carriers with HR+HER2- disease (from an adjusted model: BRCA1/2 PV 10.9%, other PV 3.6%, VUS 5.6%, negative 5.7%), while in BRCA1/2 PV carriers with triple-negative disease, platinum use did not vary significantly by genetic results ( BRCA1/2 27.7%, other PV 27.7%, VUS 20.9%, negative 20.7%; p = 0.025 for interaction between genetic result and subtype). Conclusions: Compared to women with negative genetic testing results, women with BRCA1/2 PVs more often received a platinum and/or an anthracycline in chemotherapy regimens for early-stage, HR+HER2- breast cancer. This suggests potential over-treatment. No differences in chemotherapy regimen by genetic testing result were observed in triple-negative disease.

A case-control study of healthcare disparities in sex and gender minority patients with breast cancer

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Background: Disparities in the quality of diagnosis and treatment of breast cancer in sex and gender minority (SGM) populations are largely undefined. Only 24% of studies funded by the National Cancer Institute capture data on sexual orientation, while only 10% capture data on gender identity. To address this gap, the National Academies 2020 Report calls for adding sexual orientation and gender identity (SOGI) to ongoing data collection efforts. This case-control study matching SGM patients with breast cancer to cisgender heterosexual controls is the result of linking SOGI data to the Stanford University Healthcare (SHC) Oncoshare breast cancer database, which integrates data from the electronic medical record (EMR) and California Cancer Registry. Methods: An initial database query across the SHC EMR was performed for charts containing SOGI terms in patients with breast cancer seen in SHC Oncology. 686 charts were identified for manual review and after eliminating false positives, the sample was reduced to 92 SGM patients, who were then matched by year of diagnosis, age, stage, ER-status, and HER-2 status to cisgender heterosexual controls within Oncoshare. Additional data on demographics, diagnosis, treatment, and relapse were then manually abstracted from the EMR. Results: The SGM cohort was comprised of 80% lesbians, 13% bisexuals and 6% transgender men. The median age at diagnosis across both groups was 49. SGM patients were 72% white, 4% Asian, 12% Black or Latinx 6% other compared to 63% white, 24% Asian, 6% Black or Latinx, 6% other in the controls (p = 0.0006). Thirteen percent and 32% of SGM patients engaged in risky alcohol and illicit drug use respectively, compared to 3% and 6% of controls (p = 0.028; p < 0.0001). Estrogen exposure risk factors including median age of menarche, first delivery, menopause, and use of exogenous estrogens were balanced between the two groups, but SGM patients had fewer children (median 0 vs 2, p < 0.0001). There was a delay in time to diagnosis from symptom onset in SGM patients versus controls (median 64 days vs 37 days, p = 0.043). There was no difference in surgical approach, use of post-lumpectomy radiation, or use of neoadjuvant chemotherapy for stage III disease. However, SGM patients were less likely to undergo chest reconstruction (55% vs 82%, p = 0.0098) and if ER+, to complete ≥5 years of ER-directed therapy (53% vs 72%, p = 0.048). SGM patients used more alternative medicine (46% vs 29%, p = 0.033) and had a higher rate of documented refusal of recommended oncologic treatments (38% vs 21%, p = 0.0088). Correspondingly, SGM patients experienced a higher recurrence rate (31% vs 14%, p = 0.0124). Conclusions: To our knowledge, this is the first study to examine quality of diagnosis and treatment of breast cancer in SGM patients. Several novel potential healthcare disparities are identified, which should be further evaluated in population-based studies to inform interventions.

Association of illness mindsets with health-related quality of life in cancer survivors

OBJECTIVE

This study aimed to examine the association between mindsets-established, but mutable beliefs that a person holds-and health-related quality of life in survivors of breast and gynecologic cancer.

METHOD

A cross-sectional survey study was conducted with breast and gynecologic cancer survivors. Measures included the Illness Mindset Questionnaire and Functional Assessment of Cancer Therapy-General (FACT-G).

RESULTS

Two hundred seventy-three survivors (74% breast/26% gynecologic) who were on average 3.9 years post-diagnosis (SD = 4.2), Mage 55 (SD = 12) completed the survey (response rate 80%). Of the survivors, 20.1% (N = 55) endorsed ("agree" or "strongly agree") that Cancer is a Catastrophe, 52.4% (N = 143) endorsed that Cancer is Manageable, and 65.9% (N = 180) endorsed that Cancer can be an Opportunity (not mutually exclusive). Those who endorsed a maladaptive mindset (Cancer is a Catastrophe) reported lower health-related quality of life (HRQOL) compared with those who did not hold this belief (p < .001). Alternatively, those who endorsed more adaptive mindsets (Cancer is Manageable or Cancer can be an Opportunity) reported better HRQOL compared with those who disagreed (all p-values < .05). All three mindsets were independent correlates of HRQOL, explaining 6-15% unique variance in HRQOL, even after accounting for demographic and medical factors.

CONCLUSIONS

Mindsets about illness are significantly associated with HRQOL in cancer survivors. Our data come from a one-time evaluation of cancer survivors at a single clinic and provide a foundation for future longitudinal studies and RCTs on the relationship between mindsets and psychosocial outcomes in cancer survivors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Contributions of screening, early-stage treatment, and metastatic treatment to breast cancer mortality reduction by molecular subtype in U.S. women, 2000-2017

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Background: Treatment for metastatic breast cancer has advanced since 2000, but we do not know if those advances have reduced mortality in the general population. Methods: Four Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2017 using national data on mammography use and performance, efficacy and dissemination of estrogen receptor (ER) and HER2-specific treatments of early-stage (stages I-III) and metastatic (stage IV or distant recurrence) disease, and competing mortality. Models compared overall and ER/HER2-specific breast cancer mortality rates from 2000 to 2017 relative to estimated rates with no screening or treatment, and attributed mortality reductions to screening, early-stage or metastatic treatment. Results of an exemplar model are shown. Results: The mortality reduction attributable to early-stage treatment increased from 35.8% in 2000 to 48.2% in 2017, while the proportion attributable to metastatic treatment decreased slightly from 23.9% to 20.6%. The increasing contribution of early-stage treatment reflects the transition of effective metastatic treatments to early-stage disease: accordingly, ten-year distant recurrence-free survival improved (82.5% in 2000, 87.3% in 2017; for ER+HER2+, 78.2% to 90.9%). Survival time after metastatic diagnosis also increased, doubling from 1.48 years in 2000 to 2.80 years in 2017, with the best survival for women with ER+HER2+ cancers (4.08 years) and worst for ER-HER2- (1.22 years). Conclusions: Advances in metastatic breast cancer treatment are reflected in lower population mortality, both through transition to early-stage treatment and gains for women with metastatic disease. These results may inform patient/physician discussions about breast cancer prognosis and expected benefits of treatment. [Table: see text]

Radiomic features quantifying pixel-level characteristics of breast tumors from magnetic resonance imaging predict risk factors in triple-negative breast cancer

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Background: Computationally derived quantitative imaging (radiomic) features that describe tumor phenotypes at the pixel level have demonstrated associations with clinical characteristics in early investigations of other cancers. This implies that molecular differences among tumors may be reflected in their structure on the scales probed by 3D magnetic resonance imaging (MRI). We investigated whether radiomic features computed over tumor volumes from pre-treatment breast MRI could predict risk factors in triple-negative breast cancer (TNBC). Methods: We evaluated breast tumors on pre-treatment, post-contrast T1-weighted MRI from 156 patients with non-metastatic TNBC who underwent neoadjuvant chemotherapy. Tumor regions of interest were segmented by a convolutional neural network algorithm, with validation by breast radiologists. Features quantifying tumor shape and texture were extracted for the largest tumor present in each patient. We identified 23 principal components (PCs) describing these data within the original 364-dimensional feature space for further analysis. Tumor volume was also extracted for comparison with the shape and texture PCs, clinical variables and outcomes, but was kept separate from other radiomic features, since it directly correlates with clinical stage. We compiled for the cohort clinical variables including demographics, stage, grade, and, where available, absolute lymphocyte count (ALC) and Ki-67, a cellular proliferation index routinely used in clinical practice. We then performed a series of univariate and multivariate regression analyses to identify radiomic PCs and clinical variables that significantly predict patient outcomes, and radiomic PCs that predict established risk factors. Our multivariate analyses utilized 5-fold cross-validation and Monte-Carlo determination of p-values (based on 3000 random samplings from the null hypothesis), to ensure statistical rigor in identifying predictive relationships while correcting for multiple hypothesis testing. Results: Our univariate analyses confirmed expected correlations between: overall survival and pre-treatment tumor volume (p = 0.010); survival and ALC (p = 0.002); and clinical stage and tumor volume (p = 1.2⨉10-7). From our multivariate analysis, shape and texture radiomic features were predictive of: tumor volume (p < 0.001); clinical stage (p < 0.001); and Ki-67 (p = 0.02). We confirmed that Ki-67 was predictive of post-treatment residual cancer (p = 0.014), as has been previously reported. Conclusions: Radiomic features predict breast cancer risk factors that are significant for determining outcomes for TNBC patients. Combinations of radiomic shape and texture features track closely with tumor volumes, stage, and proliferative activity, potentially reflecting underlying molecular evolution.

Constitutional BRCA1 methylation and risk of incident triple-negative breast cancer and high-grade serous ovarian cancer

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Background: About 25% of all triple-negative breast cancer (TNBC) and 10–20% of high-grade serous ovarian cancers (HGSOC) harbor BRCA1 promoter methylation. While constitutional BRCA1 promoter methylation has been observed in normal tissues of some individuals, the potential role of normal tissue methylation as a risk factor for incident TNBC or HGSOC risk is unknown. Methods: The objective of this study was to assess potential correlation between white blood cell (WBC) BRCA1 promoter methylation and subsequent risk of incident TNBC and HGSOC. To do so, we analyzed samples from women participating in the Women’s Health Initiative (WHI) study who had not been diagnosed with either breast or ovarian cancer prior to study entrance. A total of n = 636 women developing incident TNBC and 509 women developing incident HGSOC were matched with cancer-free controls (n = 1838 and 2979) in a nested case-control design. Cancers were confirmed after central medical record review. Blood samples, collected at entry, were analyzed for BRCA1 promoter methylation by massive parallel sequencing. Associations between BRCA1 methylation and incident TNBC and incident HGSOC were analyzed by unconditional logistic regression. Results: Age at entry was 62 (median; range 50 to 79) years, with median interval to diagnosis of 9 (TNBC) and 10 (HGSOC) years. The presence of methylated BRCA1 alleles was significantly associated with higher risk of incident TNBC (OR 2.44, 95% CI 1.79–3.33; P <.001) and incident HGSOC (OR 1.87, 95% CI 1.32–2.61; P <.001). Restricting analyses to individuals with > 5 years between sampling and cancer diagnosis yielded similar results (> 5 years; TNBC: OR 2.53, 95% CI 1.81–3.54; P <.001; HGSOC: OR 1.81, 95% CI 1.21–2.63; P =.003). Across individuals, methylation was not haplotype-specific, arguing against an underlying cis-acting factor. Within individuals, BRCA1 methylation was observed on the same allele, indicating clonal expansion from a single methylation event. Conclusions: Constitutional normal tissue BRCA1 promoter methylation is significantly associated with risk of incident TNBC and HGSOC with implications for prediction of these cancers not associated with germline mutations. These findings warrant further research to determine if constitutional methylation of tumor suppressor genes are pan-cancer risk factors.

Personalised Risk Prediction in Hereditary Breast and Ovarian Cancer: A Protocol for a Multi-Centre Randomised Controlled Trial

Women who test positive for an inherited pathogenic/likely pathogenic gene variant in BRCA1, BRCA2, PALB2, CHEK2 and ATM are at an increased risk of developing certain types of cancer-specifically breast (all) and epithelial ovarian cancer (only BRCA1, BRCA2, PALB2). Women receive broad cancer risk figures that are not personalised (e.g., 44-63% lifetime risk of breast cancer for those with PALB2). Broad, non-personalised risk estimates may be problematic for women when they are considering how to manage their risk. Multifactorial-risk-prediction tools have the potential to deliver personalised risk estimates. These may be useful in the patient's decision-making process and impact uptake of risk-management options. This randomised control trial (registration number to follow), based in genetic centres in the UK and US, will randomise participants on a 1:1 basis to either receive conventional cancer risk estimates, as per routine clinical practice, or to receive a personalised risk estimate. This personalised risk estimate will be calculated using the CanRisk risk prediction tool, which combines the patient's genetic result, family history and polygenic risk score (PRS), along with hormonal and lifestyle factors. Women's decision-making around risk management will be monitored using questionnaires, completed at baseline (pre-appointment) and follow-up (one, three and twelve months after receiving their risk assessment). The primary outcome for this study is the type and timing of risk management options (surveillance, chemoprevention, surgery) taken up over the course of the study (i.e., 12 months). The type of risk-management options planned to be taken up in the future (i.e., beyond the end of the study) and the potential impact of personalised risk estimates on women's psychosocial health will be collected as secondary-outcome measures. This study will also assess the acceptability, feasibility and cost-effectiveness of using personalised risk estimates in clinical care.

Breast cancer diagnosis and treatment during the COVID-19 pandemic in a nationwide, insured population

Purpose

The early months of the COVID-19 pandemic led to reduced cancer screenings and delayed cancer surgeries. We used insurance claims data to understand how breast cancer incidence and treatment after diagnosis changed nationwide over the course of the pandemic.

Methods

Using the Optum Research Database from January 2017 to March 2021, including approximately 19 million US adults with commercial health insurance, we identified new breast cancer diagnoses and first treatment after diagnosis. We compared breast cancer incidence and proportion of newly diagnosed patients receiving pre-operative systemic therapy pre-COVID, in the first 2 months of the COVID pandemic and in the later part of the COVID pandemic.

Results

Average monthly breast cancer incidence was 19.3 (95% CI 19.1–19.5) cases per 100,000 women and men pre-COVID, 11.6 (95% CI 10.8–12.4) per 100,000 in April–May 2020, and 19.7 (95% CI 19.3–20.1) per 100,000 in June 2020–February 2021. Use of pre-operative systemic therapy was 12.0% (11.7–12.4) pre-COVID, 37.7% (34.9–40.7) for patients diagnosed March–April 2020, and 14.8% (14.0–15.7) for patients diagnosed May 2020–January 2021. The changes in breast cancer incidence across the pandemic did not vary by demographic factors. Use of pre-operative systemic therapy across the pandemic varied by geographic region, but not by area socioeconomic deprivation or race/ethnicity.

Conclusion

In this US-insured population, the dramatic changes in breast cancer incidence and the use of pre-operative systemic therapy experienced in the first 2 months of the pandemic did not persist, although a modest change in the initial management of breast cancer continued.

Trends in Annual Surveillance Mammography Participation Among Breast Cancer Survivors From 2004 to 2016

BACKGROUND

Annual mammography is recommended for breast cancer survivors; however, population-level temporal trends in surveillance mammography participation have not been described. Our objective was to characterize trends in annual surveillance mammography participation among women with a personal history of breast cancer over a 13-year period.

METHODS

We examined annual surveillance mammography participation from 2004 to 2016 in a nationwide sample of commercially insured women with prior breast cancer. Rates were stratified by age group (40-49 vs 50-64 years), visit with a surgical/oncology specialist or primary care provider within the prior year, and sociodemographic characteristics. Joinpoint models were used to estimate annual percentage changes (APCs) in participation during the study period.

RESULTS

Among 141,672 women, mammography rates declined from 74.1% in 2004 to 67.1% in 2016. Rates were stable from 2004 to 2009 (APC, 0.1%; 95% CI, -0.5% to 0.8%) but declined 1.5% annually from 2009 to 2016 (95% CI, -1.9% to -1.1%). For women aged 40 to 49 years, rates declined 2.8% annually (95% CI, -3.4% to -2.1%) after 2009 versus 1.4% annually in women aged 50 to 64 years (95% CI, -1.9% to -1.0%). Similar trends were observed in women who had seen a surgeon/oncologist (APC, -1.7%; 95% CI, -2.1% to -1.4%) or a primary care provider (APC, -1.6%; 95% CI, -2.1% to -1.2%) in the prior year.

CONCLUSIONS

Surveillance mammography participation among breast cancer survivors declined from 2009 to 2016, most notably among women aged 40 to 49 years. These findings highlight a need for focused efforts to improve adherence to surveillance and prevent delays in detection of breast cancer recurrence and second cancers.

Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis

IMPORTANCE

Screening mammography and magnetic resonance imaging (MRI) are recommended for women with ATM, CHEK2, and PALB2 pathogenic variants. However, there are few data to guide screening regimens for these women.

OBJECTIVE

To estimate the benefits and harms of breast cancer screening strategies using mammography and MRI at various start ages for women with ATM, CHEK2, and PALB2 pathogenic variants.

DESIGN, SETTING, AND PARTICIPANTS

This comparative modeling analysis used 2 established breast cancer microsimulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate different screening strategies. Age-specific breast cancer risks were estimated using aggregated data from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium for 32 247 cases and 32 544 controls in 12 population-based studies. Data on screening performance for mammography and MRI were estimated from published literature. The models simulated US women with ATM, CHEK2, or PALB2 pathogenic variants born in 1985.

INTERVENTIONS

Screening strategies with combinations of annual mammography alone and with MRI starting at age 25, 30, 35, or 40 years until age 74 years.

MAIN OUTCOMES AND MEASURES

Estimated lifetime breast cancer mortality reduction, life-years gained, breast cancer deaths averted, total screening examinations, false-positive screenings, and benign biopsies per 1000 women screened. Results are reported as model mean values and ranges.

RESULTS

The mean model-estimated lifetime breast cancer risk was 20.9% (18.1%-23.7%) for women with ATM pathogenic variants, 27.6% (23.4%-31.7%) for women with CHEK2 pathogenic variants, and 39.5% (35.6%-43.3%) for women with PALB2 pathogenic variants. Across pathogenic variants, annual mammography alone from 40 to 74 years was estimated to reduce breast cancer mortality by 36.4% (34.6%-38.2%) to 38.5% (37.8%-39.2%) compared with no screening. Screening with annual MRI starting at 35 years followed by annual mammography and MRI at 40 years was estimated to reduce breast cancer mortality by 54.4% (54.2%-54.7%) to 57.6% (57.2%-58.0%), with 4661 (4635-4688) to 5001 (4979-5023) false-positive screenings and 1280 (1272-1287) to 1368 (1362-1374) benign biopsies per 1000 women. Annual MRI starting at 30 years followed by mammography and MRI at 40 years was estimated to reduce mortality by 55.4% (55.3%-55.4%) to 59.5% (58.5%-60.4%), with 5075 (5057-5093) to 5415 (5393-5437) false-positive screenings and 1439 (1429-1449) to 1528 (1517-1538) benign biopsies per 1000 women. When starting MRI at 30 years, initiating annual mammography starting at 30 vs 40 years did not meaningfully reduce mean mortality rates (0.1% [0.1%-0.2%] to 0.3% [0.2%-0.3%]) but was estimated to add 649 (602-695) to 650 (603-696) false-positive screenings and 58 (41-76) to 59 (41-76) benign biopsies per 1000 women.

CONCLUSIONS AND RELEVANCE

This analysis suggests that annual MRI screening starting at 30 to 35 years followed by annual MRI and mammography at 40 years may reduce breast cancer mortality by more than 50% for women with ATM, CHEK2, and PALB2 pathogenic variants. In the setting of MRI screening, mammography prior to 40 years may offer little additional benefit.

Polygenic risk scores for prediction of breast cancer risk in Asian populations

PURPOSE

Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups.

METHODS

The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).

RESULTS

The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.

CONCLUSION

PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Racial/Ethnic Differences in Cancer Diagnosed after Metastasis: Absolute Burden and Deaths Potentially Avoidable through Earlier Detection

BACKGROUND

Racial/ethnic disparities in cancer mortality are well described and are partly attributable to later stage of diagnosis. It is unclear to what extent reductions in the incidence of late-stage cancer could narrow these relative and absolute disparities.

METHODS

We obtained stage- and cancer-specific incidence and survival data from the Surveillance, Epidemiology, and End Results Program for persons ages 50 to 79 years between 2006 and 2015. For eight hypothetical cohorts of 100,000 persons defined by race/ethnicity and sex, we estimated cancer-related deaths if cancers diagnosed at stage IV were detected earlier, by assigning them outcomes of earlier stages.

RESULTS

We observed a 3-fold difference in the absolute burden of stage IV cancer between the group with the highest rate (non-Hispanic Black males, 337 per 100,000) and the lowest rate (non-Hispanic Asian/Pacific Islander females, 117 per 100,000). Assuming all stage IV cancers were diagnosed at stage III, 32-80 fewer cancer-related deaths would be expected across subgroups, a relative reduction of 13%-14%. Assuming one third of metastatic cancers were diagnosed at each earlier stage (I, II, and III), 52-126 fewer cancer-related deaths would be expected across subgroups, a relative reduction of 21%-23%.

CONCLUSIONS

Across population subgroups, non-Hispanic Black males have the highest burden of stage IV cancer and would have the most deaths averted from improved detection of cancer before metastasis.

IMPACT

Detecting cancer before metastasis could meaningfully reduce deaths in all populations, but especially in non-Hispanic Black populations. See related commentary by Loomans-Kropp et al., p. 512.

Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC)

We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk.

PREVENTION RELEVANCE

Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.

Abstract P5-14-01: National claims data analysis of breast cancer diagnosis and treatment before versus during the COVID-19 pandemic

Background: The COVID-19 pandemic imposed great burden on the healthcare system and has required patients and their physicians to make unprecedented choices about cancer care. Hospital-based retrospective reviews have suggested changes in breast cancer management during 2020 compared to previous years, including greater use of preoperative therapy. We used insurance claims data to understand the impact of the pandemic on breast cancer diagnosis and treatment at a national level. Methods: We identified new diagnoses of breast cancer from 2017-2020 in the Optum Clinformatics claims data set, consisting of claims records linked to electronic health records. The overall population (enrolled in Optum for at least 6 months with at least one diagnosis of any condition and no prior breast cancer diagnosis) included an average of 8 million adult Americans per year. A new breast cancer diagnosis was defined as a first-ever ICD code for breast cancer with a breast diagnostic biopsy procedure code (considered the cancer diagnosis date) within 6 months before to 3 months after that ICD code. Each year’s cohort of breast cancer cases was limited to those diagnosed between February 1 and May 30, with follow-up through June 30 of the diagnosis year. First treatment after diagnosis was classified as either endocrine therapy, chemotherapy, or surgery. Geographic area was defined by the 9 Census Bureau regions. We used a Poisson regression to compare the rate of breast cancer diagnosis in 2020 compared to 2017-2019 and a Chi-squared test to compare the distribution of first treatment in 2020 compared to 2017-2019. To investigate differences in the impact of the pandemic on rate of diagnosis (Poisson regression) or use of preoperative therapy (logistic regression) by race/ethnicity, income, or geographic area, we included each of these covariates as well as its interaction with year (2020 vs 2017-2019) in separate models. Results: There were 2,841 breast cancer diagnoses February-May 2020 (0.037% of overall population), compared to 3,880 in 2019 (0.045%), 3,509 in 2018 (0.043%), and 2,999 in 2017 (0.041%). In 2020 compared to 2017-2019, new breast cancer diagnoses decreased by 12.3% (95% CI 8.6%-15.9%; p &lt; 0.0001). No significant differences were observed in this reduction in diagnoses by race/ethnicity, income level, or geographic area. Median date of diagnosis was earlier in 2020 (March 11) compared to 2017-2019 (March 29, April 1, and April 1 respectively), a result of a larger drop in diagnoses later in the time interval in 2020. Among patients who received treatment during follow-up (83.1% in 2017-2019 vs 86.2% in 2020, a difference likely reflecting this shift in diagnosis date), there was a marked reduction in surgery as first treatment in 2020 compared to previous years (88.7% in 2017-2019 vs 69.3% in 2020), while both preoperative chemotherapy (6.1% in 2017-2019 vs 10.7% in 2020) and preoperative endocrine therapy (5.2% in 2017-2019 vs 20.1% in 2020) increased (p &lt; 0.0001). There were no differences in the shift toward preoperative therapy by race/ethnicity or income, but there was a significant difference by geographic area (p=0.0003): the Mountain region had least change in use of preoperative therapy (odds ratio 2.46 [95% CI 1.75-3.47] of preoperative therapy during vs before the pandmic) while the Middle Atlantic region had the greatest (odds ratio 5.64 [95% CI 3.79-8.38]). Conclusions: Among insured U.S. patients, new breast cancer diagnoses decreased by 12.3% during February-May 2020 compared to the same period in the previous three years, and use of preoperative therapy, largely endocrine, increased by 2.7-fold. The impact of the pandemic on choice of first treatment differed by geographic area, but not by race/ethnicity or income in this insured population. We will monitor with continued follow-up of claims data to assess the longer-term impact of these pandemic-related changes on treatment patterns, cost, and patient outcomes.

Citation Format: Jennifer L Caswell-Jin, Maryam N Shafaee, Lan Xiao, Mina Liu, Natasha Purington, Esther M John, Melissa L Bondy, Allison W Kurian. National claims data analysis of breast cancer diagnosis and treatment before versus during the COVID-19 pandemic [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-14-01.

Abstract P5-14-03: National claims data analysis of breast cancer diagnosis and treatment before versus during the COVID-19 pandemic

Background: The COVID-19 pandemic imposed great burden on the healthcare system and required patients and their physicians to make unprecedented choices about cancer care. Hospital-based retrospective reviews suggested changes in breast cancer management during 2020 compared to previous years, including greater use of preoperative therapy. We used insurance claims data to understand the impact of the pandemic on breast cancer diagnosis and treatment at a national level. Methods: We identified new diagnoses of breast cancer from 2017-2020 in the Optum data set, consisting of claims records linked to electronic health records. The overall population (enrolled in Optum for at least 6 months with at least one diagnosis of any condition and no prior breast cancer diagnosis) included an average of 8 million adult Americans per year. A breast cancer diagnosis was defined as a first-ever ICD code for breast cancer with a breast diagnostic biopsy procedure code (considered the cancer diagnosis date) within 6 months before to 3 months after that ICD code. Each year’s cohort of breast cancer cases was limited to those diagnosed between February 1 and May 30, with follow-up through June 30 of the diagnosis year. First treatment after diagnosis was classified as either endocrine therapy, chemotherapy, or surgery. Geographic area was defined by the 9 Census Bureau regions. We used a Poisson regression to compare the rate of breast cancer diagnosis in 2020 versus 2017-2019 and a Chi-squared test to compare the distributions of first treatment. To investigate differences in the impact of the pandemic on rate of diagnosis (Poisson regression) or use of preoperative therapy (logistic regression) by race/ethnicity, income, or geographic area, we included each of these covariates as well as its interaction with year (2020 vs 2017-2019) in separate models. Results: There were 2,841 breast cancer diagnoses February-May 2020 (0.037% of overall population), compared to 3,880 in 2019 (0.045%), 3,509 in 2018 (0.043%), and 2,999 in 2017 (0.041%). In 2020 compared to 2017-2019, new breast cancer diagnoses decreased by 12.3% (95% CI 8.6%-15.9%; p &lt; 0.0001). No significant differences were observed in this reduction in diagnoses by race/ethnicity, income level, or geographic area. Median date of diagnosis was earlier in 2020 (March 11) compared to 2017-2019 (March 29-April 1), a result of the diagnosis rate dropping more in later months. Among patients who received treatment during follow-up (83.1% in 2017-2019 vs 86.2% in 2020, a difference likely reflecting this shift in diagnosis date), there was a marked reduction in surgery as first treatment in 2020 compared to previous years (88.7% in 2017-2019 vs 69.3% in 2020), while both preoperative chemotherapy (6.1% in 2017-2019 vs 10.7% in 2020) and preoperative endocrine therapy (5.2% in 2017-2019 vs 20.1% in 2020) increased (p &lt; 0.0001). There were no differences in the shift toward preoperative therapy by race/ethnicity or income, but there was a significant difference by geographic area (p=0.0003): the Mountain region had the least change in use of preoperative therapy (odds ratio 2.46 [95% CI 1.75-3.47] of preoperative therapy during vs before the pandemic) while the Middle Atlantic region had the greatest (odds ratio 5.64 [95% CI 3.79-8.38]). Conclusions: Among insured U.S. patients, new breast cancer diagnoses decreased by 12.3% during February-May 2020 compared to the same period in the previous three years, and use of preoperative therapy, largely endocrine, increased by 2.7-fold. The impact of the pandemic on choice of first treatment differed by geographic area, but not by race/ethnicity or income in this insured population. We will monitor with continued follow-up of claims data to assess the longer-term impact of these pandemic-related changes on treatment patterns, cost, and patient outcomes.

Citation Format: Jennifer L Caswell-Jin, Maryam N Shafaee, Lan Xiao, Mina Liu, Natasha Purington, Esther M John, Melissa L Bondy, Allison W Kurian. National claims data analysis of breast cancer diagnosis and treatment before versus during the COVID-19 pandemic [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-14-03.

Abstract ES3-2: Clinical management of moderate penetrance genes

Genetic epidemiologic studies have found that germline pathogenic mutations in the following 12 genes confer a substantial (two-fold or higher) increase in breast cancer risk: ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C, RAD51D and TP53. Mutations in several of these genes, including the notable examples of ATM, CHEK2 and PALB2, are classified as “moderate penetrance”: conferring a relative risk of breast cancer that is between two-times and five-times higher than that of the average woman. Next-generation sequencing technology and lower costs have enabled the widespread use of multiplex germline testing panels that contain both high and moderate penetrance genes. Recent studies have reported a relatively high prevalence of ATM, CHEK2 and PALB2 mutations among unselected breast cancer patients (approximately 0.5-1% per gene), including among women diagnosed at older ages. ATM and CHEK2 mutations are associated with elevated risks of hormone receptor-positive breast cancer, while PALB2 mutations are more associated with triple-negative disease. Important recent developments in the clinical management of patients with moderate penetrance gene mutations include model-based estimates of optimal breast screening protocols; evidence suggesting the safety of adjuvant radiotherapy; emerging data on susceptibility to targeted therapy with poly(ADP-ribose) polymerase inhibitors; and characterization of the risks of developing other, non-breast cancers. Ongoing studies and future research priorities will be discussed.

Citation Format: AW Kurian. Clinical management of moderate penetrance genes [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr ES3-2.

Clinician-Reported Impact of Germline Multigene Panel Testing on Cancer Risk Management Recommendations

Background

With increased adoption of multi-gene panel testing (MGPT) for hereditary cancer, management guidelines now include a wider range of predisposition genes. Yet little is known about whether MGPT results prompt changes to clinicians’ risk management recommendations and whether those recommendations adhere to guidelines.

Methods

We assessed cancer risk management recommendations made by clinicians ordering MGPT for hereditary cancer at a diagnostic laboratory using an internet-based survey. We received paired pre- and posttest responses for 2172 patients (response rate = 14.3%). Unpaired posttest responses were received in 168 additional patients with positive results. All tests were 2-sided.

Results

Clinicians reported a change in risk management recommendations for 76.6% of patients who tested positive for a pathogenic or likely pathogenic variant, with changes to surveillance being most common (71.1%), followed by surgical (33.6%), chemoprevention (15.1%), and clinical trial (9.4%) recommendations. Clinicians recommended risk-reducing interventions more often for patients with pathogenic variants in high-risk than moderate-risk genes (P < .001), whereas surveillance recommendations were similar for high-risk and moderate-risk genes. Guideline adherence was high for surveillance (86.3%) and surgical (79.6%) recommendations. Changes to risk management recommendations occurred in 8.8% and 7.6% of patients with uncertain and negative results, respectively.

Conclusions

Clinicians report frequent changes to cancer risk management recommendations based on positive results in both high-risk and moderate-risk genes. Reported introduction of interventions in patients with inconclusive and negative results is rare and adherence to practice guidelines is high in patients with positive results, suggesting a low probability of harm resulting from MGPT.

Rare germline copy number variants (CNVs) and breast cancer risk

Germline copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Gene burden tests detected the strongest association for deletions in BRCA1 (P = 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P = 0.0008), ATM (P = 0.002) and BRCA2 (P = 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. To the best of our knowledge, this is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.