Integrated Multi-Scale Analysis of Tumor and Host Response to Therapy

许多因素导致癌症治疗失败，包括肿瘤对药物缺乏反应，对宿主的毒性，肿瘤在避难所部位的生长以及对药物的耐药性的出现等。 通常情况下，这些因素是单独研究，但它是他们的行动一致，最终使病人。因此，采取综合和系统的方法来研究肿瘤和宿主如何随着时间的推移以及对各种癌症干预措施的反应的相互作用至关重要。项目4致力于多尺度测量宿主对癌症及其治疗的反应，并将此信息与其他项目测量的肿瘤反应整合到淋巴瘤和白血病的综合虚拟癌症模型（VCM）中。 我们的宿主水平的测量将集中在肿瘤-宿主相互作用的两个关键方面：宿主免疫反应和介导细胞间通讯的细胞因子。宿主免疫反应动态的系统级测量将使用一种新的自组装高密度蛋白质微阵列平台获得，血清细胞因子水平将使用高度敏感的磁纳米蛋白质芯片技术进行监测。通过仔细协调小鼠模型、时间点和治疗条件与肿瘤水平测量（在RPS中），我们将获得一个新的数据集， 在淋巴瘤和白血病的两个重要的体内肿瘤模型中肿瘤和宿主的进展。 这个协调的肿瘤/宿主（TH）数据集将是开发肿瘤和宿主之间功能相互作用的计算TH模型的基础。TH模型将揭示肿瘤/宿主变量的低维子集，其需要基于其过去的状况来预测未来的TH状态。此外，通过整合TH模型与分子细胞肿瘤模型（来自RP 1 -3），我们将开发一个全面的，预测性的白血病和淋巴瘤虚拟癌症模型（VCM）。将使用VCM 在细胞、肿瘤和宿主水平上预测疾病对特定分子干预的时间演变。然后，VCM预测将反馈给所有RP，以便通过实验验证模型预测。我们设想，经过验证的虚拟癌症模型将使分子靶向治疗方案的评估，以合理地达到最有前途的治疗策略，攻击淋巴瘤和白血病。

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.