Treatment Patterns, Safety, and Patient Reported Outcomes among Adult Women with Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer with or without, or with Unknown, BRCA1/2 Mutation(s): Results of a Real-World Study from the United States, United Kingdom, and four EU Countries

Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration

Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine.

Breast Cancer and Genetic BRCA1/2 Testing in Routine Clinical Practice: Why, When and For Whom?

Pathogenic variants of the tumor suppressor genes BRCA1 and BRCA2 are responsible for the majority of hereditary breast cancers; they are also becoming increasingly important to identify whether patients are suitable for targeted therapy with poly ADP-ribose polymerase inhibitors (PARPi). Patients with HER2-negative breast cancer and BRCA1/2 germline mutations can benefit significantly from PARPi therapy, and the findings of the OlympiAD and the EMBRACA phase III clinical trials for regulatory approval were recently expanded by the addition of the most recent OlympiA data on the treatment of patients with early disease and a high risk of recurrence. This means that BRCA1/2 germline testing to plan patient therapy is now also relevant for patients with early breast cancer and therefore has a direct impact on survival. Healthcare research data shows, however, that BRCA1/2 testing rates are strongly affected by familial history, cancer subtype (particularly triple-negative subtypes), and patient age at onset of disease (especially with regards to younger patients with breast cancer), despite the existing clear recommendations for BRCA1/2 germline testing to identify whether PARPi therapy is indicated. This article presents the clinical implications of identifying BRCA1/2 germline mutations in patients with breast cancer, the current recommendations on molecular diagnostics, and their implementation in practice. The treatment of patients with breast cancer has progressed greatly in recent years and now offers individual treatment concepts which can only be implemented after the targeted identification of individual parameters. As detection of a BRCA1/2 germline mutation is essential for planning individual therapy, where indicated, testing should be arranged as early as possible. It is the only way of identifying patients suitable for PARPi therapy and ensuring they receive the best possible treatment. This also applies to patients with a negative familial history, HR-positive disease, or who are older at onset of disease.