Precision Oncology in Northern Germany: Integrating Multi-Omics and Artificial Intelligence to Support Evidence-Based...

The expanding availability of multi-omics profiling and advances in artificial intelligence (AI) and machine learning (ML) are changing precision oncology. Molecular testing strategies such as longitudinal liquid biopsy assessment, germline variant analysis, and the evaluation of tumor-infiltrating clonal hematopoiesis are gaining attention, given their potential to inform treatment decisions from surveillance through therapy selection.

In 2025, during a NORD (Northern Oncology, Research and Development) workshop on "Precision Oncology," stakeholders from three Northern German University hospitals exchanged insights on the impact of tumor heterogeneity and the tumor microenvironment, approaches for multi-layer data integration, and strategies to accelerate translation using innovative clinical trial designs. Building on this discussion, participants evaluated how the integration of multi-omics data, AI, and ML into Molecular Tumor Board (MTB) workflows could enhance decision-making, as well as the challenges associated with implementing these tools in routine clinical care. The workshop thereby not only identified key opportunities and obstacles but also formulated recommendations on how these tools can be stepwise integrated into existing workflows to generate the evidence required for their implementation in clinical practice.

This review connects expert perspectives with targeted literature to outline how these emerging methods advance precision oncology. It further identifies the technical, structural, and clinical requirements for their implementation in clinical use, and highlights how they can improve the quality, speed and accessibility of MTB recommendations.