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2 We assessed the PREDICT v 2.2 for prognosis of breast cancer patients with pathogenic germline BRCA1 3 and BRCA2 variants, using follow-up data from 5453 BRCA1/2 carriers from the Consortium of Investigators 4 of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC). 5 PREDICT for estrogen receptor (ER)-negative breast cancer had modest discrimination for BRCA1 carrier 6 patients overall (Gönen & Heller unbiased concordance 0.65 in CIMBA, 0.64 in BCAC), but it distinguished 7 clearly the high-mortality group from lower risk categories. In an analysis of low to high risk categories by 8 PREDICT score percentiles, the observed mortality was consistently lower than the expected mortality, but 9 the confidence intervals always included the calibration slope. Altogether, our results encourage the use of 10 the PREDICT ER-negative model in management of breast cancer patients with germline BRCA1 variants. 11 For the PREDICT ER-positive model, the discrimination was slightly lower in BRCA2 variant carriers 12 (concordance 0.60 in CIMBA, 0.65 in BCAC). Especially, inclusion of the tumor grade distorted the 13 prognostic estimates. The breast cancer mortality of BRCA2 carriers was under-estimated at the low end of 14 the PREDICT score distribution, whereas at the high end, the mortality was over-estimated. These data 15 suggest that BRCA2 status should also be taken into consideration with tumor characteristics, when 16 estimating the prognosis of ER-positive breast cancer patients. 17 Keywords: PREDICT, breast cancer, prognosis, BRCA1, BRCA2, validation, survival

Peer reviewed: True, OBJECTIVE: To describe national patterns of National Health Service (NHS) analysis of mismatch repair (MMR) genes in England using individual-level data submitted to the National Disease Registration Service (NDRS) by the NHS regional molecular genetics laboratories. DESIGN: Laboratories submitted individual-level patient data to NDRS against a prescribed data model, including (1) patient identifiers, (2) test episode data, (3) per-gene results and (4) detected sequence variants. Individualised per-laboratory algorithms were designed and applied in NDRS to extract and map the data to the common data model. Laboratory-level MMR activity audit data from the Clinical Molecular Genetics Society/Association of Clinical Genomic Science were used to assess early years' missing data. RESULTS: Individual-level data from patients undergoing NHS MMR germline genetic testing were submitted from all 13 English laboratories performing MMR analyses, comprising in total 16 722 patients (9649 full-gene, 7073 targeted), with the earliest submission from 2000. The NDRS dataset is estimated to comprise >60% of NHS MMR analyses performed since inception of NHS MMR analysis, with complete national data for full-gene analyses for 2016 onwards. Out of 9649 full-gene tests, 2724 had an abnormal result, approximately 70% of which were (likely) pathogenic. Data linkage to the National Cancer Registry demonstrated colorectal cancer was the most frequent cancer type in which full-gene analysis was performed. CONCLUSION: The NDRS MMR dataset is a unique national pan-laboratory amalgamation of individual-level clinical and genomic patient data with pseudonymised identifiers enabling linkage to other national datasets. This growing resource will enable longitudinal research and can form the basis of a live national genomic disease registry.