Many cancer patients are under or over treated as a consequence of insufficient knowledge of therapy response now. Gene-expression signatures and DNA-panel sequencing will most likely not be enough to predict therapeutic responses. Thus, there is an urgent demand for discovery of alternative treatment prediction methods to facilitate personalized oncology.
Additionally, the current research models for majority of solid tumors are largely relied on either 2-dimensional (2D) cell cultures, which provide great insight into the tumor growth mechanisms but could not represent the complex interactions between the cancer cells and their environment, or animal models that fulfill the physiological study conditions but sporadically fail to recapitulate the same drug responses from human beings. Moreover, it takes up to several weeks or month before animal models could provide biological data, even not to mention the insufficient correlation between expected and observed results. Thus, artificial matrix scaffold based ex vivo 3D cell culture models have been developed over recent years as an attempt to fill the gap in between.
We have successfully established our unselected ex vivo 3D cell culture (WTC) model from clinical breast tumor materials, which could act as a good and efficient platform for anti-cancer drug screening and validation. In combination with next generation sequencing (NGS) and other functional analyses, WTC could provide us a platform to identify drug sensitivity and resistance. Currently, we have an ongoing clinical validation study for its predictive value for breast cancer patients at the Stockholm South General hospital (Södersjukhuset).