Background: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are... Show moreBackground: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. Methods: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. Results: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. Conclusions: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility. Show less
Purpose: Patients may transfer of hospital for clinical reasons but this may delay time to treatment. The purpose of this study is to provide insight in the extent of hospital transfer in breast... Show morePurpose: Patients may transfer of hospital for clinical reasons but this may delay time to treatment. The purpose of this study is to provide insight in the extent of hospital transfer in breast cancer care; which type of patients transfer and what is the impact on time to treatment.Methods: We included 41,413 breast cancer patients registered in the Netherlands Cancer Registry between 2014 and 2016. We investigated transfer of hospital between diagnosis and first treatment being surgery or neoadjuvant chemotherapy (NAC). Co-variate adjusted characteristics predictive for hospital transfer were determined. To adjust for possible treatment by indication bias we used propensity score matching (PSM). Time to treatment in patients with and without hospital transfer was compared.Results: Among 41,413 patients, 8.5% of all patients transferred to another hospital between diagnosis and first treatment; 4.9% before primary surgery and 24.8% before NAC. Especially young (aged <40 years) patients and those who underwent a mastectomy with immediate breast reconstruction (IBR) were more likely to transfer. The association of mastectomy with IBR with hospital transfer remained when using PSM. Hospital transfer after diagnosis significantly prolonged time to treatment; breast conserving surgery by 5 days, mastectomy by 7 days, mastectomy with IBR by 9 days and NAC by 1 day.Conclusions: While almost 5% of Dutch patients treated with primary surgery transfer hospital after diagnosis and up to 25% for patients treated with NAC, our findings suggest that especially those treated with primary surgery are at risk for additional treatment delay by hospital transfer. (C) 2018 Published by Elsevier Ltd. Show less
