Systems Biology of Diversity in Cancer

描述（由申请人提供）：现代癌症研究中的一个关键问题是遗传和非遗传多样性如何影响肿瘤进展和对治疗的反应。MSKCC的癌症系统生物学中心（CCSB）组建了一个研究人员联盟，他们整合了计算和实验策略，以研究单个细胞，肿瘤微环境和患者水平上的癌症多样性。该CCSB的研究计划分为四个相互关联的子项目。(I)We研究肿瘤发生过程中细胞对生长因子和药物反应的变异性，使用黑色素瘤和乳腺癌细胞中生长因子IL-6的竞争作为模型系统。我们使用IL-6通路的动力学的计算建模，以优化依赖于细胞多样性的新的化疗方案，以最大限度地提高肿瘤细胞毒性。(II)我们使用肿瘤和基质细胞中蛋白酶网络的表达谱和肿瘤相关巨噬细胞亚群的全基因组谱来学习肿瘤-基质细胞相互作用的预测统计模型。我们还使用基于代理的计算模型来模拟癌细胞巨噬细胞的相互作用。将通过体外和体内实验验证计算预测。我们专注于不同肿瘤亚型中信号信息流差异的预测网络模型。利用系统药物扰动实验的结果，我们将设计基于非线性微分方程的Hopfield网络模型，以破译原发性胶质母细胞瘤亚型之间的信号网络差异，并研究耐药性演变过程中网络动力学的变化。(IV)我们研究慢性淋巴细胞白血病（CLL）患者B细胞信号通路的内源性多样性。我们将生成信号通路的生化模型，以确定其表达变化预测功能异质性的关键信号调节因子，用单细胞分析验证预测，并定义一组新的功能标记物，以更好地表征疾病进展。通过阐明癌症多样性的后果，这项研究将最终指导新癌症疗法的开发。

DESCRIPTION (provided by applicant): A critical issue in modern cancer research is how diversity, both genetic and non-genetic, influences tumor progression and response to therapy. The Center for Cancer Systems Biology (CCSB) at MSKCC assembles a consortium of investigators who integrate computational and experimental strategies to investigate diversity in cancer at the level of individual cells, tumor microenvironment, and patients. The research program for this CCSB is organized into four inter-related subprojects. (I)We study the variability of cellular responses to growth factors and drugs during tumorigenesis, using competition for the growth factor IL-6 in melanoma and breast cancer cells as a model system. We use computational modeling of the dynamics of the IL-6 pathway in order to optimize new chemotherapeutic protocols that rely on cellular diversity to maximize tumor cytotoxicity. (II) We use expression profiling of protease networks in both tumor and stromal cells and genome-wide profiling of subpopulations of tumor-associated macrophages to learn predictive statistical models of tumor-stromal cell interactions. We also use agent-based computational models to simulate cancer-cell macrophage interactions. Computational predictions will be validated with in vitro and in vivo experiments. (Ill)We focus on predictive network models of differences in signaling information flow in distinct tumor subtypes. Using the results of systematic drug perturbation experiments, we will design Hopfield network models based on non-linear differential equations to decipher the differences in signaling networks between primary glioblastoma subtypes and to investigate the changes in network dynamics during the evolution of drug resistance. (IV) We study the endogenous diversity of B cell signaling pathways in Chronic Lymphocytic Leukemia (CLL) patients. We will generate biochemical models of signaling pathways to identify key signaling regulators whose variation in expression predicts functional heterogeneity, validate the predictions with single-cell profiling, and define a new set of functional markers to better characterize disease progression. By elucidating the consequences of diversity in cancer, this research will ultimately guide the development of new cancer therapies.