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Predicting clinical response to anticancer drugs using 3D-bioprinted tumor models for personalized therapy

Project description

Recreating a patient's tumour ecoystem with 3D printing will foster precision treatment

One of the key problems with experimentation in model systems is the adequacy of the model system in representing the human disease or condition to be evaluated. In the case of cancer, it has become clear that the interaction of tumour cells with their microenvironment is critical to disease progression, metastasis and response to therapy. 3DCanPredict is developing a novel analytical system utilising a 3D bioprinted tumour model exploiting hydrogel scaffolds and a patient's own tumour cells. With functional blood vessels that can be infused with the patient's serum via a pump, not only does the system mimic the tumour microenvironment but it opens the door to personalised cancer medicine.


Predicting clinical response to novel and existing anticancer drugs remains a major hurdle for successful cancer treatment. Studies indicate that the tumor ecosystem, resembling an organ-like structure, can limit the predictive power of current therapies that were evaluated solely on tumor cells. The interactions of tumor cells with their adjacent microenvironment are required to promote tumor progression and metastasis, determining drug responsiveness. Such interactions do not form in standard research techniques, where cancer cells grow on 2D plastic dishes. Hence, there is a need to develop new cancer models that better mimic the physio-pathological conditions of tumors. Here, we create 3D-bioprinted tumor models based on a library of hydrogels we developed as scaffold for different tumor types, designed according to the mechanical properties of the tissue of origin. As PoC, we bioprinted a vascularized 3D brain tumor model from brain tumor cells co-cultured with stromal cells and mixed with our hydrogels, that resemble the biophysics of the tumor and its microenvironment. Our patient-derived models consist of cells from a biopsy, constructed according to CT/MRI scans, and include functional vessels allowing for patients' serum to flow when connected to a pump. These models will facilitate reproducible, reliable and rapid results, determining which treatment suits best the specific patient's tumor. Taken together, this 3D-printed model could be the basis for potentially replacing cell and animal models. We predict that this powerful platform will be used in translational research for preclinical evaluation of new therapies and for clinical drug screening, which will save critical time, reduce toxicity and significantly decrease costs generating a major societal benefit. Our platform offers a highly attractive business case, as pharmaceutical and biotech companies heavily invest in preclinical predictive tools for novel personalized drug screening strategies.

Host institution

Net EU contribution
€ 150 000,00
Total cost
No data

Beneficiaries (1)