Systems Biology of Diversity in Cancer

现代癌症研究中的一个关键问题是，遗传和非遗传的多样性如何影响肿瘤 进展和对治疗的反应。MSKCC癌症系统生物学中心(CCSB)组装 将计算和实验策略结合起来调查多样性的研究人员组成的联盟 在肿瘤的个体细胞、肿瘤微环境和患者的水平上。这方面的研究计划 CCSB分为四个相互关联的子项目。(I)我们研究了细胞反应的可变性 肿瘤形成过程中的生长因子和药物，在黑色素瘤和肿瘤中竞争生长因子IL-6 以乳腺癌细胞为模型系统。我们使用IL-6途径动力学的计算模型 为了优化新的化疗方案，这些方案依赖于细胞多样性来最大化肿瘤 细胞毒性。(Ii)我们在肿瘤和间质细胞中都使用了蛋白酶网络的表达谱，并 肿瘤相关巨噬细胞亚群的全基因组图谱以了解预测性统计学 肿瘤-间质细胞相互作用的模型。我们还使用了基于代理的计算模型来模拟 癌细胞与巨噬细胞的相互作用。计算预测将在体外和体内得到验证 实验。(ILL)我们专注于信令信息流差异的预测网络模型 不同的肿瘤亚型。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组的Hopfield网络模型破译信令差异 原发胶质母细胞瘤亚型之间的网络和研究网络动力学的变化 耐药性的演变。(四)我们研究了B细胞信号通路的内源性多样性 慢性淋巴细胞白血病(CLL)患者。我们将生成信号通路的生化模型 为了确定其表达差异可以预测功能异质性的关键信号调节因子，验证 与单细胞图谱的预测，并定义了一组新的功能标记，以更好地表征 疾病的发展。通过阐明癌症多样性的后果，这项研究最终将 引导肿瘤新疗法研发。

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 growrth factor IL-6 in melanoma and breast cancer cells as a model system. We use computational modeling ofthe dynamics ofthe 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.