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多样性如何影响肿瘤 进展和治疗反应。MSKCC癌症系统生物学中心（CCSB） 一个将计算和实验策略结合起来研究多样性的研究者联盟 在单个细胞、肿瘤微环境和患者水平上的癌症。这个研究项目 CCSB分为四个相互关联的子项目。(I)我们研究了细胞反应的变异性， 生长因子和药物在肿瘤发生过程中，使用竞争生长因子IL-6在黑色素瘤， 乳腺癌细胞作为模型系统。我们使用IL-6通路动力学的计算模型 为了优化依赖于细胞多样性的新的化疗方案， 细胞毒(II)我们使用肿瘤和基质细胞中蛋白酶网络的表达谱， 肿瘤相关巨噬细胞亚群的全基因组分析，以了解预测性统计学 肿瘤-基质细胞相互作用的模型。我们还使用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进行验证 实验(Ill)我们专注于预测网络模型的差异，在信令信息流， 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的Hopfield网络模型，用于破译信令中的差异 原发性胶质母细胞瘤亚型之间的网络，并研究网络动态变化， 抗药性的进化(IV)我们研究了在不同发育阶段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。我们将建立信号通路的生化模型 为了鉴定其表达变化预测功能异质性的关键信号调节因子，验证 预测与单细胞分析，并定义了一组新的功能标志物，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。

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.