Patient Derived Scaffolds (PDS) in Cancer Research and Drug Discovery

Breast cancer is a common malignancy with varying clinical behaviors and for the more aggressive subtypes, novel and more efficient therapy approaches are needed. Qualities of the tumor microenvironment as well as the presence of subtypes of cancer cells with various properties have separately been associated with malignant clinical features and better understanding of the interplay between these two key features could potentially reveal novel targetable key events linked to cancer progression. Since the cancer microenvironment influences tumor progression and metastasis it is further essential to include in the design of in vivo-like cancer models.

Current preclinical testing platforms for cancer drug development are mainly limited to 2D cell culture systems that poorly mimic physiological environments and traditional, low throughput animal models. Few models truly include the cancer microenvironments allowing the influence of the microenvironment to be monitored. We have therefore developed a patient derived scaffold (PDS) model that is based on cell-free patient scaffolds that can be repopulated with reporter cancer cells. The changes in the adapting cancer cells can then be monitored and specific influences on various key tumour biological properties can be enumerated. Within this project we have further defined the protein compositions of the cell-free scaffolds allowing us to construct 3D-printed surrogate scaffolds based on key proteins and agarose based prints, mimicking human like growth conditions facilitating high-throughput screenings of cancer drug candidates.

In this webinar, findings using PDS from breast and colon cancer will be described and links to clinical properties as well drug fingerprint based on in vivo-like growth conditions will be presented. The results using optimized 3D-printed surrogate scaffolds will also be delineated clearly illustrating the importance of including the cancer microenvironment in diagnostic and treatment predictive efforts as well as in the design of novel cancer drugs.