Assessment of BRCA1 and BRCA2 Germline Variant Data From Patients With Breast Cancer in a Real-World Data Registry.

PURPOSE: The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. METHODS: Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. RESULTS: In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. CONCLUSION: We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date.

Association between adjuvant radiation treatment and breast cancer-specific mortality among older women with comorbidity burden: A comparative effectiveness analysis of SEER-MHOS.

BACKGROUND: The National Comprehensive Cancer Network suggested that older women with low-risk breast cancer (LRBC; i.e., early-stage, node-negative, and estrogen receptor-positive) could omit adjuvant radiation treatment (RT) after breast-conserving surgery (BCS) if they were treated with hormone therapy. However, the association between RT omission and breast cancer-specific mortality among older women with comorbidity is not fully known. METHODS: 1105 older women (≥65 years) with LRBC in 1998-2012 were queried from the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey data resource and were followed up through July 2018. Latent class analysis was performed to identify comorbidity burden classes. A propensity score-based inverse probability of treatment weighting (IPTW) was applied to Cox regression models to obtain subdistribution hazard ratios (HRs) and 95% CI for cancer-specific mortality considering other causes of death as competing risks, overall and separately by comorbidity burden class. RESULTS: Three comorbidity burden (low, moderate, and high) groups were identified. A total of 318 deaths (47 cancer-related) occurred. The IPTW-adjusted Cox regression analysis showed that RT omission was not associated with short-term, 5- and 10-year cancer-specific death (p = 0.202 and p = 0.536, respectively), regardless of comorbidity burden. However, RT omission could increase the risk of long-term cancer-specific death in women with low comorbidity burden (HR = 1.98, 95% CI = 1.17, 3.33), which warrants further study. CONCLUSIONS: Omission of RT after BCS is not associated with an increased risk of cancer-specific death and is deemed a reasonable treatment option for older women with moderate to high comorbidity burden.