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Abstract Background Inhibition of androgen receptor (AR) signaling is the main treatment strategy in advanced prostate cancer (PCa). A subset of castration resistant prostate cancer (CRPC) bypasses the AR blockade by increased fibroblast growth factor receptor (FGFR) signaling. The first‐ and second‐generation, non‐covalent FGFR inhibitors (FGFRis) have largely failed in the clinical trials against PCa. Purpose: In this study, we tested the drug sensitivity of LNCaP, VCaP, and CWR‐R1PCa cell lines to second‐generation, covalent FGFRis (FIIN1, FIIN2) and a novel FGFR downstream molecule inhibitor (FRS2αi). Methods 2D and 3D mono‐ and co‐cultures of cancer cells, and cancer‐associated fibroblasts (CAFs) were used to mimic tumor‐stroma interactions in the extracellular matrix (ECM). The treatment responses of the FGFR signaling molecules, the viability and proliferation of cancer cells, and CAFs were determined through immunoblotting, migration assay, cell viability assay, and real‐time imaging. Immunofluorescent and confocal microscopy images of control and treated cultures of cancer cells and CAFs, and their morphometric data were deduced. Results The FGFRis were more effective in mono‐cultures of the cancer cells compared with co‐cultures with CAFs. The FRS2αi was specifically effective in co‐cultures with CAFs but was not cytotoxic to CAF mono‐cultures as in the case of FIIN1 and FIIN2. At the molecular level, FRS2αi decreased p‐FRS2α, p‐ERK1/2, and activated apoptosis as monitored by cleaved caspase‐3 activity in a concentration‐dependent manner in the co‐cultures. We observed no synergistic drug efficacy in the combination treatment of the FGFRi with ARi, enzalutamide, and darolutamide. The FRS2αi treatment led to a decrease in proliferation of cancer cell clusters in co‐cultures as indicated by their reduced size and Ki67 expression. Conclusions CAFs exert a protective effect on cancer cells and should be included in the in vitro models to make them physiologically more relevant in screening and testing of FGFRis. The FRS2αi was the most potent agent in reducing the viability and proliferation of the 3D organotypic co‐cultures, mainly by disrupting the contact between CAFs and cancer cell clusters. The next‐generation FGFRi, FRS2αi, may be a better alternative treatment option for overcoming ARi treatment resistance in advanced PCa.

IntroductionThe potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes.Materials and methodsOur proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network.ResultsIn a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria’s national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested.DiscussionThe presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge.