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Background Multiple different evidence types as well as gene-specific variant classification guidelines need to be considered during the classification of variants, making the process complex. Therefore, tools that support variant classification by experts are urgently needed. Methods We present HerediVar a web application and HerediClassify a variant classification algorithm. The performance of HerediClassify was validated and compared to other variant classification tools. HerediClassify implements 19/28 variant classification criteria by the American College of Medical Genetics and gene-specific recommendations for ATM , BRCA1 , BRCA2 , CDH1 , PALB2 , PTEN , and TP53 . Results HerediVar offers modular annotation services and allows for collaboration in the classification of variants. On the validation dataset, HerediClassify shows an average F1-Score of 93% across all criteria. HerediClassify outperforms other automated variant classification tools like vaRHC and Cancer SIGVAR. Conclusion In HerediVar and HerediClassify we present a powerful solution to support variant classification in HBOC. Through their modular design, HerediVar and HerediClassify are easily extendable to other use cases and human genetic diagnostics as a whole.

Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.

Patient similarity is a fundamental concept in precision oncology, offering a pathway to personalized medicine by identifying patterns and shared characteristics among patients. This concept enables stratification into clinically meaningful subgroups, prediction of treatment responses, and the tailoring of therapeutic interventions to individual needs. Despite its transformative potential, the definition, measurement, and clinical application of patient similarity remain inconsistently established, creating challenges in its integration into cancer research and clinical practice. This study aimed to synthesize evidence on the multidimensional concept of patient similarity in cancer research by analyzing its application across different points of possible data types, methodological frameworks, biological contexts, and commonly studied cancer types. This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework and the Joanna Briggs Institute guidelines. A systematic search was conducted across PubMed, MEDLINE, LIVIVO, and Web of Science (covering the period from 1998 to February 2024) and was supplemented by snowball sampling and manual searches. Duplicate records were removed, and study selection was carried out in 3 phases: title and abstract screening, disagreement resolution, and full-text screening. Each step was independently performed by 2 reviewers in Rayyan, with conflicts resolved by a third reviewer. Data extraction was performed using a predefined template to capture methodological approaches, data types, cancer types, and research objectives related to similarity in patients with cancer. This scoping review synthesized evidence from 137 studies, emphasizing the multidimensional concept of patient similarity in cancer research, which integrates diverse data types, methodological frameworks, research objectives, and cancer types. Transcriptomic data (92/137, 67.1%) and clinical data (65/137, 47.4%) were the most frequently used, often combined to enhance the comprehensiveness of similarity analyses. Machine learning (76/137, 55.5%) and network-based approaches (72/137, 52.5%) were prominent methods, reflecting their capacity to handle complex, high-dimensional data and uncover intricate relationships. Cancer subtype identification (70/137, 51.1%) and biomarker discovery (41/137, 29.9%) were the primary research objectives, underscoring the centrality of patient similarity in precision oncology. Breast, lung, and brain cancers were the most frequently studied, benefiting from established research frameworks and abundant datasets. Conversely, rare cancers were underrepresented, highlighting a critical gap in the generalizability of current methodologies. This comprehensive scoping review examines the concept of patient similarity in cancer research and highlights the critical role of a multilayered perspective in capturing its complexity and identification to enhance understanding and application in precision oncology.