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Urokinase-type plasminogen activator is widely discussed as a marker for cancer prognosis and diagnosis and as a target for cancer therapies. Together with its receptor, uPA plays an important role in tumorigenesis, tumor progression and metastasis. In the present study, systematic evolution of ligands by exponential enrichment (SELEX) was used to select single-stranded DNA aptamers targeting different forms of human uPA. Selected aptamers allowed the distinction between HMW-uPA and LMW-uPA, and therefore, presumably, have different binding regions. Here, uPAapt-02-FR showed highly affine binding with a K-D of 0.7 nM for HMW-uPA and 21 nM for LMW-uPA and was also able to bind to pro-uPA with a K-D of 14 nM. Furthermore, no cross-reactivity to mouse uPA or tissue-type plasminogen activator (tPA) was measured, demonstrating high specificity. Suppression of the catalytic activity of uPA and inhibition of uPAR-binding could be demonstrated through binding with different aptamers and several of their truncated variants. Since RNA aptamers are already known to inhibit uPA-uPAR binding and other pathological functions of the uPA system, these aptamers represent a novel, promising tool not only for detection of uPA but also for interfering with the pathological functions of the uPA system by additionally inhibiting uPA activity.
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.