Legacy Genetic Testing Results for Cancer Susceptibility: How Common are Conflicting Classifications in a Large Variant Dataset from Multiple Practices?

PURPOSE: The classification of germline variants may differ between labs and change over time. We apply a variant harmonization tool, Ask2Me VarHarmonizer, to map variants to ClinVar and identify discordant variant classifications in a large multipractice variant dataset. METHODS: A total of 7496 variants sequenced between 1996 and 2019 were collected from 11 clinical practices. Variants were mapped to ClinVar, and lab-reported and ClinVar variant classifications were analyzed and compared. RESULTS: Of the 4798 unique variants identified, 3699 (77%) were mappable to ClinVar. Among mappable variants, variants of unknown significance (VUS) accounted for 74% of lab-reported classifications and 60% of ClinVar classifications. Lab-reported and ClinVar discordances were present in 783 unique variants (21.2% of all mappable variants); 121 variants (2.5% of all unique variants) had within-practice lab-reported discordances; and 56 variants (1.2% of all unique variants) had lab-reported discordances across practices. The unmappable variants were associated with a higher proportion of lab-reported pathogenic classifications (50% vs. 21%, p < 0.0001) and a lower proportion of lab-reported VUS classifications (46% vs. 74%, p < 0.0001). CONCLUSIONS: Our study shows that discordant variant classification occurs frequently, which may lead to inappropriate recommendations for prophylactic treatments or clinical management.

American Joint Committee on Cancer's Staging System for Breast Cancer, Eighth Edition: What the Radiologist Needs to Know.

The TNM staging system for cancer was developed by Pierre Denoix in France in the 1940s and 1950s. The North American effort to standardize the TNM system for cancer staging was first organized in 1959 as the American Joint Committee for Cancer Staging and End-Results Reporting, which is now the American Joint Committee on Cancer (AJCC). The most recent edition of the AJCC Cancer Staging Manual, the eighth edition, was globally adopted on January 1, 2018. Previous editions of the manual have relied on anatomic methods of staging alone, which used population-based survival data to predict clinical outcomes. In the era of precision medicine, the major change in the eighth edition is the incorporation of prognostic biomarkers to more accurately predict clinical outcomes and treatment response on an individual basis, without relying solely on the anatomic extent of disease. Factors such as tumor grade, hormone receptor and oncogene expression, and multigene panel recurrence scores are now integrated with anatomic information to yield a final prognostic stage group, which will provide better stratification of patient prognosis. The purpose of this article is to review the major changes in the AJCC eighth edition for breast cancer staging, review anatomic TNM staging, familiarize the radiologist with prognostic biomarkers and prognostic staging, and identify key sites of disease that may alter clinical management. ©RSNA, 2018.

The impact of patient age on breast cancer risk prediction models.

BACKGROUND: The impact of age on breast cancer risk model calculations at the population level has not been well documented. METHODS: Retrospective analysis of formal breast cancer risk assessment in 36 542 females ages 40-84 at a single institution from 02/2007 to 12/2009. Five-year and lifetime breast cancer risks were calculated using Gail, Tyrer-Cuzick version 6 (TC6), Tyrer-Cuzick version 7 (TC7), BRCAPRO, and Claus models. Risk of BRCA mutation was calculated using BRCAPRO, TC6, TC7, and Myriad. Eligibility for BRCA testing was assessed using NCCN guidelines. Descriptive analyses were performed and trends in risk were assessed by age. RESULTS: The lifetime risk of breast cancer trended down with increasing age in all risk models. TC7 calculated the highest estimates for lifetime risk for all age ranges and had the highest proportion of patients with a calculated lifetime risk >20%. Five-year risk increased with age in all models. By age 60-64, every risk model predicted a mean 5-year risk ≥1.7%. Myriad estimated >5% risk of BRCA mutation more often than other models for all ages. Risk of BRCA mutation stayed constant with age with Myriad, but trended down with increasing age with TC6, TC7, and BRCAPRO. CONCLUSIONS: More patients have an estimated lifetime risk of breast cancer >20% and qualify for MRI screening with the Tyrer-Cuzick model. All models predict an increased 5-year risk with age, which could impact chemoprevention recommendations. To maximize access to genetic testing, the Myriad model and NCCN guidelines should be used.

Combining Breast Cancer Risk Prediction Models.

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

Bias in the reporting of family history: implications for clinical care.

Family history of cancer is critical for identifying and managing patients at risk for cancer. However, the quality of family history data is dependent on the accuracy of patient self reporting. Therefore, the validity of family history reporting is crucial to the quality of clinical care. A retrospective review of family history data collected at a community hospital between 2005 and 2009 was performed in 43,257 women presenting for screening mammography. Reported numbers of breast, colon, prostate, lung, and ovarian cancer were compared in maternal relatives vs. paternal relatives and in first vs. second degree relatives. Significant reporting differences were found between maternal and paternal family history of cancer, in addition to degree of relative. The number of paternal family histories of cancer was significantly lower than that of maternal family histories of cancer. Similarly, the percentage of grandparents' family histories of cancer was significantly lower than the percentage of parents' family histories of cancer. This trend was found in all cancers except prostate cancer. Self-reported family history in the community setting is often influenced by both bloodline of the cancer history and the degree of relative affected. This is evident by the underreporting of paternal family histories of cancer, and also, though to a lesser extent, by degree. These discrepancies in reporting family history of cancer imply we need to take more care in collecting accurate family histories and also in the clinical management of individuals in relation to hereditary risk.

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes.

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40-84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2-. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.

Relationship of established risk factors with breast cancer subtypes.

BACKGROUND: Breast cancer is a heterogeneous disease, divided into subtypes based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Subtypes have different biology and prognosis, with accumulating evidence of different risk factors. The purpose of this study was to compare breast cancer risk factors across tumor subtypes in a large, diverse mammography population. METHODS: Women aged 40-84 without a history of breast cancer with a screening mammogram at three United States health systems from 2006 to 2015 were included. Risk factor questionnaires were completed at mammogram visit, supplemented by electronic health records. Invasive tumor subtype was defined by immunohistochemistry as ER/PR+HER2-, ER/PR+HER2+, ER, and PR-HER2+, or triple-negative breast cancer (TNBC). Cox proportional hazards models were run for each subtype. Associations of race, reproductive history, prior breast problems, family history, breast density, and body mass index (BMI) were assessed. The association of tumor subtypes with screen detection and interval cancer was assessed using logistic regression among invasive cases. RESULTS: The study population included 198,278 women with a median of 6.5 years of follow-up (IQR 4.2-9.0 years). There were 4002 invasive cancers, including 3077 (77%) ER/PR+HER2-, 300 (8%) TNBC, 342 (9%) ER/PR+HER2+, and 126 (3%) ER/PR-HER2+ subtype. In multivariate models, Black women had 2.7 times higher risk of TNBC than white women (HR = 2.67, 95% CI 1.99-3.58). Breast density was associated with increased risk of all subtypes. BMI was more strongly associated with ER/PR+HER2- and HER2+ subtypes among postmenopausal women than premenopausal women. Breast density was more strongly associated with ER/PR+HER2- and TNBC among premenopausal than postmenopausal women. TNBC was more likely to be interval cancer than other subtypes. CONCLUSIONS: These results have implications for risk assessment and understanding of the etiology of breast cancer subtypes. More research is needed to determine what factors explain the higher risk of TNBC for Black women.

Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort.

BACKGROUND: Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. METHODS: We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40-84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. RESULTS: Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. CONCLUSIONS: In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.

Identification and management of women at high risk for hereditary breast/ovarian cancer syndrome.

Despite advances in identifying genetic markers of high risk patients and the availability of genetic testing, it remains challenging to efficiently identify women who are at hereditary risk and to manage their care appropriately. HughesRiskApps, an open-source family history collection, risk assessment, and Clinical Decision Support (CDS) software package, was developed to address the shortcomings in our ability to identify and treat the high risk population. This system is designed for use in primary care clinics, breast centers, and cancer risk clinics to collect family history and risk information and provide the necessary CDS to increase quality of care and efficiency. This paper reports on the first implementation of HughesRiskApps in the community hospital setting. HughesRiskApps was implemented at the Newton-Wellesley Hospital. Between April 1, 2007 and March 31, 2008, 32,966 analyses were performed on 25,763 individuals. Within this population, 915 (3.6%) individuals were found to be eligible for risk assessment and possible genetic testing based on the 10% risk of mutation threshold. During the first year of implementation, physicians and patients have fully accepted the system, and 3.6% of patients assessed have been referred to risk assessment and consideration of genetic testing. These early results indicate that the number of patients identified for risk assessment has increased dramatically and that the care of these patients is more efficient and likely more effective.

Which risk model to use? Clinical implications of the ACS MRI screening guidelines.

The American Cancer Society (ACS) guidelines define the appropriate use of MRI as an adjunct to mammography for breast cancer screening. Three risk assessment models are recommended to determine if women are at sufficient risk to warrant the use of this expensive screening tool, however, the real-world application of these models has not been explored. We sought to understand how these models behave in a community setting for women undergoing mammography screening. We conducted a retrospective analysis of 5,894 women, who received mammography screening at a community hospital and assessed their eligibility for MRI according to the ACS guidelines. Of the 5,894 women, 342 (5.8%) were eligible for MRI, but we found significant differences in the number of eligible women identified by each model. Our results indicate that these models identify very different populations, implying that the ACS guidelines deserve further development and consideration.