A concept of a prognostic system for personalized anti-tumor therapy based on supermodeling

Abstract Application of computer simulation for predicting cancer progression/remission/ recurrence is still underestimated by clinicians. This is mainly due to the lack of tumor modeling approaches, which are both reliable and realistic computationally. We present here the concept of a viable prediction/correction system for predicting tumor dynamics. It is very similar, in spirit, to that used in weather forecast and climate modeling. The system is based on the supermodeling technique where the supermodel consists of a few coupled instances (sub-models) of a generic coarse-grained tumor model. Consequently, the latent and fine-grained cancer properties, not included in the generic model, e.g., reflecting microscopic phenomena and other unpredictable events influencing tumor dynamics, are hidden in sub-models coupling parameters, which can be learned from incoming real data. Thus instead of matching hundreds of parameters for multi-scale tumor models, we need to fit only several values of coupling coefficients between sub-models to simulate the current tumor status.