Empowering personalized oncology: evolution of digital support and visualization tools for molecular tumor boards.

Background: Molecular tumor boards (MTBs) play a pivotal role in personalized oncology, leveraging complex data sets to tailor therapy for cancer patients. The integration of digital support and visualization tools is essential in this rapidly evolving field facing fast-growing data and changing clinical processes. This study addresses the gap in understanding the evolution of software and visualization needs within MTBs and evaluates the current state of digital support.

Genomic and phenotypic stability of fusion-driven pediatric sarcoma cell lines.

Human cancer cell lines are the mainstay of cancer research. Recent reports showed that highly mutated adult carcinoma cell lines (mainly HeLa and MCF-7) present striking diversity across laboratories and that long-term continuous culturing results in genomic/transcriptomic heterogeneity with strong phenotypical implications. Here, we hypothesize that oligomutated pediatric sarcoma cell lines mainly driven by a fusion transcription factor, such as Ewing sarcoma (EwS), are genetically and phenotypically more stable than the previously investigated adult carcinoma cell lines.

Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

Introduction: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system.