Integrating models to quantify environment mediated drug resistance

Drug resistance is the single most important driver of cancer treatment failure for modern targeted therapies, and the dialogue between tumor and stroma has been shown to modulate the response to molecularly targeted therapies through proliferative and survival signaling. In this work, we investigate interactions between a growing tumor and its surrounding stroma and their role in facilitating the emergence of drug resistance. We used mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating intrinsic and extrinsic components of resistance in BRAF-mutated melanoma; the model describes tumor-stroma dynamics both with and without treatment. Integration of experimental data into our model revealed significant variation in either the intensity of stromal promotion or intrinsic tissue carrying capacity across animal replicates.

Major Findings. Through the integration of a simple mathematical model with in vitro and in vivo experimental growth dynamics of melanoma cell lines (both with and without drug), we were able to dissect the relative contributions of intrinsic versus environmental resistance. Our study revealed significant heterogeneity in vivo, indicating that there is a diversity of either stromal promotion or tumor carrying capacity under targeted therapy. We believe this variation may be one possible explanation for the heterogeneity observed across patients and within individual patients with multiple metastases. Therefore, quantifying this variation both within in vivo model systems and in individual patients could have a significant impact on the design of future treatment strategies that target both tumor and stroma. Further, we present guidelines for building more effective and longer lasting therapeutic strategies utilizing our experimentally calibrated model. These strategies explicitly consider the protective nature of the stroma and utilize inhibitors that modulate it.

from # All Medicine by Alexandros G. Sfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2u6ayAP
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