Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells

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PROJECT SUMMARY Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells, PI Tan Developing biomimetic systems that capture the heterogeneity of cancer-signaling cascades will enhance the development of effective anti-cancer therapy and reduce the attrition rate of drug candidates. A cancer- signaling cascade consists of receptors that propagate signals to protein networks, which then modulate expression profiles of gene networks. A signaling cascade can exhibit tremendous heterogeneity in cancers due to variation in the concentration, composition, and sequence of its protein constituents. Such heterogeneity has been known to diminish the efficacy of certain anti-cancer drugs. To date, however, there is a general lack of engineered systems that emulate the heterogeneity of cancer-signaling cascades directly. Even though cell cultures and xenografts capture physical structure of tumors, they do not directly control the heterogeneity of a targeted signaling cascade. Here, we propose to overcome the bottleneck by engineering a biomimetic-cell approach that reconstitutes variants of a cancer-signaling cascade to mimic its heterogeneity. Each biomimetic cell is a synthetic system that will be constructed from the bottom-up by mimicking cell membranes and one instance of a heterogeneous cancer-signaling cascade. As a proof of concept, we will investigate the proposed idea using the Ras signaling cascade activated by the platelet-derived-growth-factor receptor beta (PDGFRβ). The proposal consists of two main steps 1) Reconstitute the core PDGFRβ signaling cascade inside biomimetic cells. 2) Model heterogeneity of the PDGFRβ signaling cascade inside biomimetic cells. We will construct a library of biomimetic cells, each containing one unique instance of the signaling cascade, using a bio-printer that will mix each protein constituents of the signaling cascade at well-defined composition and concentrations. The biomimetic-cell library and a computational model will be integrated to study robustness of anti-cancer drugs in inhibiting the heterogeneous signaling cascade. The proposed idea challenges the main paradigm in studies of cancer-signaling cascades by reconstituting (partially or fully) at least 100,000 unique and physiologically relevant instances of a cancer-signaling cascade inside biomimetic cells, which will be miniaturized for high-throughput drug screening. The proposed work is significant because it enables multi- dimensional screening of drugs against well-defined heterogeneity of the targeted cancer-signaling cascade. If successful, this study will yield a validated, generalizable approach for studying the impact of heterogeneous cancer pathways on the efficacy of anti-cancer drugs.