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Abstract Background Due to the abundant usage of chemotherapy in young triple-negative breast cancer (TNBC) patients, the unbiased prognostic value of BRCA1-related biomarkers in this population remains unclear. In addition, whether BRCA1-related biomarkers modify the well-established prognostic value of stromal tumor-infiltrating lymphocytes (sTILs) is unknown. This study aimed to compare the outcomes of young, node-negative, chemotherapy-naïve TNBC patients according to BRCA1 status, taking sTILs into account. Methods We included 485 Dutch women diagnosed with node-negative TNBC under age 40 between 1989 and 2000. During this period, these women were considered low-risk and did not receive chemotherapy. BRCA1 status, including pathogenic germline BRCA1 mutation (gBRCA1m), somatic BRCA1 mutation (sBRCA1m), and tumor BRCA1 promoter methylation (BRCA1-PM), was assessed using DNA from formalin-fixed paraffin-embedded tissue. sTILs were assessed according to the international guideline. Patients’ outcomes were compared using Cox regression and competing risk models. Results Among the 399 patients with BRCA1 status, 26.3% had a gBRCA1m, 5.3% had a sBRCA1m, 36.6% had tumor BRCA1-PM, and 31.8% had BRCA1-non-altered tumors. Compared to BRCA1-non-alteration, gBRCA1m was associated with worse overall survival (OS) from the fourth year after diagnosis (adjusted HR, 2.11; 95% CI, 1.18–3.75), and this association attenuated after adjustment for second primary tumors. Every 10% sTIL increment was associated with 16% higher OS (adjusted HR, 0.84; 95% CI, 0.78–0.90) in gBRCA1m, sBRCA1m, or BRCA1-non-altered patients and 31% higher OS in tumor BRCA1-PM patients. Among the 66 patients with tumor BRCA1-PM and ≥ 50% sTILs, we observed excellent 15-year OS (97.0%; 95% CI, 92.9–100%). Conversely, among the 61 patients with gBRCA1m and

Abstract Background 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. Methods 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. Results 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. Conclusions 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.

Abstract 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 (