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An inherited single nucleotide variant (SNV) in the 5′UTR of the BRCA1 gene c.-107A > T was identified to be related to BRCA1 promoter hypermethylation and a hereditary breast and ovarian cancer phenotype in two UK families. We investigated whether this BRCA1 variant was also present in a Dutch cohort of breast and ovarian cancer patients with tumor BRCA1 promoter hypermethylation. We selected all breast and ovarian cancer cases that tested positive for tumor BRCA1 promoter hypermethylation at the Netherlands Cancer Institute and Sanger sequenced the specific mutation in the tumor DNA. In total, we identified 193 tumors with BRCA1 promoter hypermethylation in 178 unique patients. The wild-type allele was identified in 100% (193/193) of sequenced tumor samples. In a large cohort of 178 patients, none had tumors harboring the previously identified c.-107A > T SNV in BRCA1. We therefore can conclude that the germline SNV is not pervasive in patients with tumor BRCA1 promoter hypermethylation.

High-dose chemotherapy with autologous stem cell rescue (HDCT) is a promising treatment for patients with stage III, HER2-negative, homologous recombination deficient (HRD) breast cancer. Clinical effectiveness and cost-effectiveness are currently under investigation in an international multicenter randomized controlled trial. To increase the chance of successful introduction of HDCT into daily clinical practice, we aimed to identify relevant factors for smooth implementation using an early comprehensive assessment framework.This is a qualitative, multi-stakeholder, exploratory research using semi-structured interviews guided by the Constructive Technology Assessment model, which evaluates the quality of a novel health technology by clinical, economic, patient-related, and organizational factors. Stakeholders were recruited by purposeful stratified sampling and interviewed until sufficient content saturation was reached. Two researchers independently created themes, categories, and subcategories by following inductive coding steps, these were verified by a third researcher.We interviewed 28 stakeholders between June 2019 and April 2021. In total, five overarching themes and seventeen categories were identified. Important findings for optimal implementation included the structural identification and referral of all eligible patients, early integration of supportive care, multidisciplinary collaboration between- and within hospitals, (de)centralization of treatment aspects, the provision of information for patients and healthcare professionals, and compliance to new regulation for the BRCA1-like test.In anticipation of a positive reimbursement decision, we recommend to take the highlighted implementation factors into consideration. This might expedite and guide high-quality equitable access to HDCT for patients with stage III, HER2-negative, HRD breast cancer in the Netherlands.

An inherited single nucleotide variant (SNV) in the 5'UTR of the BRCA1 gene c.-107A T was identified to be related to BRCA1 promoter hypermethylation and a hereditary breast and ovarian cancer phenotype in two UK families. We investigated whether this BRCA1 variant was also present in a Dutch cohort of breast and ovarian cancer patients with tumor BRCA1 promoter hypermethylation. We selected all breast and ovarian cancer cases that tested positive for tumor BRCA1 promoter hypermethylation at the Netherlands Cancer Institute and Sanger sequenced the specific mutation in the tumor DNA. In total, we identified 193 tumors with BRCA1 promoter hypermethylation in 178 unique patients. The wild-type allele was identified in 100% (193/193) of sequenced tumor samples. In a large cohort of 178 patients, none had tumors harboring the previously identified c.-107A T SNV in BRCA1. We therefore can conclude that the germline SNV is not pervasive in patients with tumor BRCA1 promoter hypermethylation.

Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in breast cancer grading. Computer-based breast cancer grading methods have been proposed but only work on specifically selected tissue areas and/or require labor-intensive annotations to be applied to new datasets. In this study, we trained and evaluated a deep learning-based breast cancer grading model that works on whole-slide histopathology images. The model was developed using whole-slide images from 706 young (lt; 40 years) invasive breast cancer patients with corresponding tumor grade (low/intermediate vs. high), and its constituents nuclear grade, tubule formation and mitotic rate. The performance of the model was evaluated using Cohen's kappa on an independent test set of 686 patients using annotations by expert pathologists as ground truth. The predicted low/intermediate (n = 327) and high (n = 359) grade groups were used to perform survival analysis. The deep learning system distinguished low/intermediate versus high tumor grade with a Cohen's Kappa of 0.59 (80% accuracy) compared to expert pathologists. In subsequent survival analysis the two groups predicted by the system were found to have a significantly different overall survival (OS) and disease/recurrence-free survival (DRFS/RFS) (p lt; 0.05). Univariate Cox hazard regression analysis showed statistically significant hazard ratios (p lt; 0.05). After adjusting for clinicopathologic features and stratifying for molecular subtype the hazard ratios showed a trend but lost statistical significance for all endpoints. In conclusion, we developed a deep learning-based model for automated grading of breast cancer on whole-slide images. The model distinguishes between low/intermediate and high grade tumors and finds a trend in the survival of the two predicted groups.