Search

Genetic testing in patients with ovarian carcinoma (OC) is crucial, as around 10-15% of these women have a genetic predisposition to OC. Although guidelines have recommended universal germline testing for all patients with OC for a decade, implementation has proved challenging, thus resulting in low germline-testing rates (around 30-50%). Many new initiatives to improve genetic-testing rates have emerged, but most have been carried out at the local level, leading to differences in workflows within and between countries. We present an example of a nationwide implementation project that has successfully led to a uniform, high-quality genetic-testing workflow for women with OC. Nationwide multidisciplinary meetings generated consensus on the preferred workflow for OC genetic testing: the "Tumor-First" workflow. This workflow means starting by testing the tumor DNA for the presence of pathogenic variants in OC-risk genes, thus providing a prescreen to germline testing while yielding information on the effectiveness of treatment with PARP inhibitors. This new workflow efficiently stratifies genetic counseling and germline testing and reduces healthcare costs. Although challenging, the nationwide implementation of this workflow was successful, resulting in tumor-DNA testing rates exceeding 80%. In this article, we present our structured implementation approach, illustrate our implementation strategies-which were tailored to identified factors important to implementation-and share the lessons learned from the Tumor-First implementation project. This knowledge could facilitate the future implementation of workflows aimed at optimizing the recognition of hereditary cancers., (© 2024. The Author(s).)

Objective: Genetic testing in epithelial ovarian cancer (OC) is essential to identify a hereditary cause like a germline BRCA1/2 pathogenic variant (PV). An efficient strategy for genetic testing in OC is highly desired. We evaluated costs and effects of two strategies; (i) Tumor-First strategy, using a tumor DNA test as prescreen to germline testing, and (ii) Germline-First strategy, referring all patients to the clinical geneticist for germline testing., Methods: Tumor-First and Germline-First were compared in two scenarios; using real-world uptake of testing and setting implementation to 100%. Decision analytic models were built to analyze genetic testing costs (including counseling) per OC patient and per family as well as BRCA1/2 detection probabilities. With a Markov model, the life years gained among female relatives with a germline BRCA1/2 PV was investigated., Results: Focusing on real-world uptake, with the Tumor-First strategy more OC patients and relatives with a germline BRCA1/2 PV are detected (70% versus 49%), at lower genetic testing costs (€1898 versus €2502 per patient, and €2511 versus €2930 per family). Thereby, female relatives with a germline BRCA1/2 PV can live on average 0.54 life years longer with Tumor-First compared to Germline-First. Focusing on 100% uptake, the genetic testing costs per OC patient are substantially lower in the Tumor-First strategy (€2257 versus €4986)., Conclusions: The Tumor-First strategy in OC patients is more effective in identifying germline BRCA1/2 PV at lower genetic testing costs per patient and per family. Optimal implementation of Tumor-First can further improve detection of heredity in OC patients., Competing Interests: Declaration of Competing Interest Vera M. Witjes, Marjolijn J.L. Ligtenberg, Janet R. Vos, Jozé C.C. Braspenning, Marian J.E. Mourits, Joanne A. de Hullu, Eddy M.M. Adang, and Nicoline Hoogerbrugge declare no conflict of interest. Margreet G.E.M. Ausems received a grant from AstraZeneca for development and filming of a training module regarding mainstreaming of genetic testing in OC, which is unrelated to the current manuscript., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The new 'Tumor-First' workflow for genetic testing in epithelial ovarian cancer detects women with a genetic predisposition effectively and efficiently. 'Tumor-First' indicates that the tumor DNA of all patients diagnosed with epithelial ovarian cancer is examined for the presence of pathogenic variants in, amongst others, BRCA1 and BRCA2 genes. Women with an aberrant tumor DNA test result are eligible for genetic testing using a sample of blood to detect heredity. Simultaneously, the tumor DNA test provides information on the effectiveness of additional treatment with PARP inhibitors. Thereby, the Tumor-First workflow saves time and healthcare costs. This workflow is currently implemented on a national level. The focus of implementation is transferring knowledge to healthcare professionals on requesting, interpreting and reporting the tumor DNA test and providing adequate support. This nationwide implementation of Tumor-First can improve the recognition of heredity in epithelial ovarian cancer.

Universal tumor DNA testing in epithelial ovarian cancer patients can function not only as an efficient prescreen for hereditary cancer testing, but may also guide treatment choices. This innovation, introduced as Tumor-First workflow, offers great opportunities, but ensuring optimal multidisciplinary collaboration is a challenge. We investigated factors that were relevant and important for large-scale implementation. In three multidisciplinary online focus groups, healthcare professionals (gynecologic oncologists, pathologists, clinical geneticists, and clinical laboratory specialists) were interviewed on factors critical for the implementation of the Tumor-First workflow. Recordings were transcribed for analysis in Atlas.ti according to the framework of Flottorp that categorizes seven implementation domains. Healthcare professionals from all disciplines endorse implementation of the Tumor-First workflow, but more detailed standardization and advice regarding the logistics of the workflow were needed. Healthcare professionals explored ways to stay informed about the different phases of the workflow and the results. They emphasized the importance of including all epithelial ovarian cancer patients in the workflow and monitoring this inclusion. Overall, healthcare professionals would appreciate supporting material for the implementation of the Tumor-First workflow in the daily work routine. Focus group discussions have revealed factors for developing a tailored implementation strategy for the Tumor-First workflow in order to optimize care for epithelial ovarian cancer patients. Future innovations affecting multidisciplinary oncology teams including clinical geneticists can benefit from the lessons learned., (© 2022. The Author(s).)

Background: Histology restricted genetic predisposition testing of ovarian carcinoma patients is a topic of debate as the prevalence of BRCA1/2 pathogenic variants (PVs) in various histological subtypes is ambiguous. Our primary aim was to investigate the proportion of germline BRCA1/2 PVs per histological subtype. Additionally, we evaluated (i) proportion of somatic BRCA1/2 PVs and (ii) proportion of germline PVs in other ovarian carcinoma risk genes., Methods: PubMed, EMBASE and Web of Science were systematically searched and we included all studies reporting germline BRCA1/2 PVs per histological subtype. Pooled proportions were calculated using a random-effects meta-analysis model. Subsets of studies were used for secondary analyses., Results: Twenty-eight studies were identified. The overall estimated proportion of germline BRCA1/2 PVs was 16.8% (95% CI 14.6 to 19.2). Presence differed substantially among patients with varying histological subtypes of OC; proportions being highest in high-grade serous (22.2%, 95% CI 19.6 to 25.0) and lowest in clear cell (3.0%, 95% CI 1.6 to 5.6) and mucinous (2.5%, 95% CI 0.6 to 9.6) carcinomas. Somatic BRCA1/2 PVs were present with total estimated proportion of 6.0% (95% CI 5.0 to 7.3), based on a smaller subset of studies. Germline PVs in BRIP1, RAD51C, RAD51D, PALB2, and ATM were present in approximately 3%, based on a subset of nine studies., Conclusion: Germline BRCA1/2 PVs are most frequently identified in high-grade serous ovarian carcinoma patients, but are also detected in patients having ovarian carcinomas of other histological subtypes. Limiting genetic predisposition testing to high-grade serous ovarian carcinoma patients will likely be insufficient to identify all patients with a germline PV., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Irreproducibility is a well-recognized problem in biomedical animal experimentation. Phenotypic variation in animal models is one of the many challenging causes of irreproducibility. How to deal with phenotypic variation in experimental designs is a topic of debate. Both reducing and embracing variation are highlighted as strategies for reproducibility. In this theoretical review, we use variation in mouse microbiome composition as an example to analyze this ongoing discussion, drawing on both animal research and philosophy of science. We provide a conceptual explanation of reproducibility and analyze how the microbiome affects mouse phenotypes to demonstrate that the role of the microbiome in irreproducibility can be understood in two ways: (i) the microbiome can act as a confounding factor, and (ii) the result may not be generalizable to mice harboring a different microbiome composition. We elucidate that reducing variation minimizes confounding, whereas embracing variation ensures generalizability. These contrasting strategies make dealing with variation in experimental designs extremely complex. Here, we conclude that the most effective strategy depends on the specific research aim and question. The field of biomedical animal experimentation is too broad to identify a single optimal strategy. Thus, dealing with variation should be considered on a case-by-case basis, and awareness amongst researchers is essential.