3D bioprinted tissue-like cores for cancer diagnostics

Mateu Pla


    Mateu Pla


    IBEC, Instituto de Bioingeniería de Cataluña, Spain


    According to the WHO, by 2025 more than 19 million new cases of cancer are expected to be diagnosed worldwide. Most cases are confirmed by the histopathological analysis of a biopsy. This information is crucial, in addition, to decide the most appropriate treatment. However, histological techniques face some degree of variability that can lead to misinterpretation, resulting in diagnostic errors, inaccurate patient treatment and high costs for the healthcare system. More reliable control samples are required to reduce variability and improve accuracy.

    To take advantage of 3D bioprinters to create 3D tissue-like structures containing biomarkers that can be used as quality controls in histopathological analysis in companion diagnostics kits.

    Problem to Solve
    Histopathological cancer diagnostics have to be accurate in order to deliver the correct treatment for each individual patient, but analysis is often imprecise due to assay variability, which causes differences in how biomarker levels are interpreted. For this reason, biomarker assays require quality control samples processed side-by-side with patient samples to verify the diagnosis.
    Currently, quality controls come from surplus human tissue with a known expression of the required biomarker. However, these samples are scarce and non-homogeneous, and their use raises ethical issues.
    3D bioprinting can create customised 3D structures containing cells embedded in a support matrix. Using this technology, 3DBIOcores containing different cell lines can be produced and used as control samples for several molecular biomarkers related to cancer detection.

    Level of Innovation
    3DBIOcores will be a real innovation in histopathology analysis. They will be processed side-by-side with the relevant sample, serving as both a positive and a negative control. In addition, they can be mass produced in a standard model, improving efficiency and reducing costs.