Many factors contribute to treatment failure in cancer including lack of tumor response to the drugs, toxicity to the host, tumor growth in sanctuary sites and the emergence of resistance to drugs among others. Typically, these factors are studied individually, but it is their action in concert that ultimately overwhelms the patient. Therefore, it is of paramount importance to take an integrated and systems approach to studying the interaction of how both the tumor and host respond over time and in response to various cancer interventions. Project 4 is dedicated to multi-scale measurements of the host response to cancer and its therapy and integrating this information with the tumor responses measured by the other projects into a comprehensive Virtual Cancer Model (VCM) of lymphoma and leukemia. Our host-level measurements will focus on two critical aspects of tumor-host interactions: host immune response and cytokines that mediate intercellular communication. Systems-level measurements of the dynamics of the host immune response will be obtained using a novel self-assembling high density protein microarray platform, and serum cytokine levels will be monitored using a highly sensitive magneto nano protein chip technology. Through careful coordination of mouse models, time points and treatment conditions with tumor-level measurements (in RPS), we will acquire a novel dataset that tracks the progression of the tumor and the host in two important in vivo tumor models of lymphoma and leukemia. This coordinated tumor/host (TH) dataset will be the foundation for developing a computational TH model of the functional interactions between tumor and host. The TH model will uncover a low dimensional subset of tumor/host variables needed to predict the future TH state based on the its past condition. In addition, by integrating the TH model with the molecular-cellular-tumor model (from RP1-3), we will develop a comprehensive, predictive Virtual Cancer Model (VCM) for leukemia and lymphoma. The VCM will be used to predict the temporal evolution of the disease at the cell, tumor and host levels in response to specific molecular interventions. VCM predictions will then feed back to all RPs in order to experimentally validate model predictions. We envision that the validated Virtual Cancer Model would enable the evaluation of moleculariy targeted treatment regimens to arrive rationally at the most promising therapeutic strategy for attacking lymphoma and leukemia.
Cancer progression and its response to treatments hinges on how the evolving tumor interacts with the host. Results from this project will provide new insights into tumor-host interactions in leukemia and lymphoma. These insights will be used to develop an in silico model of these diseases. We envision that the in silico Virtual Cancer Model may be used to evaluate moleculariy targeted treatment regimens to arrive at the most promising therapeutic strategy for tackling lymphoma and leukemia.
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