Variability of Cellular Responses to Growth Factors and Drugs During Tumorgenesis

在肿瘤发生期间，癌细胞以及健康细胞依赖于肿瘤细胞中产生的生长因子。 细胞以自分泌和旁分泌方式维持其增殖和分化。因此，存在一场“拔河” 肿瘤和周围非癌细胞之间的生长因子，以及每个细胞的适应性 单个细胞必须在群体水平上通过这种竞争来定义。重要的是，癌细胞在 肿瘤的前缘的特征在于具有化学抗性，具有增强的转移潜力， 在形成新肿瘤方面非常有效[6]。 我们已经确定癌细胞和邻近的基质细胞表达高水平的生长因子IL-6， 和活化的Stat 3，它们被假设为肿瘤发生和转移的主要介质。 进展在这项提案中，我们专注于黑色素瘤和乳腺癌细胞中的IL-6/pStat 3通路， 其增殖和分化依赖于pStat 3信号。在理解的背景下， 肿瘤动力学，特别是在药物治疗期间， 将研究细胞的遗传学-遗传学群体。对结果表型的理解 肿瘤细胞的多样性将导致根除肿瘤群体的治疗干预的改善。 具体而言，我们将（1）描述肿瘤细胞对生长因子的可变反应，并靶向 抑制剂：（2）设计一个预测细胞多样性后果的生物学框架， 最佳治疗干预，最大限度地提高根除肿瘤的机会;（3）验证 预测细胞系和小鼠模型中的细胞骨架。该项目充分利用了我们的 癌症生物学和临床经验（Bromberg）、单细胞分析和生化建模方面的专业知识 （Altan-Bonnet）和几何建模（Michor）。我们将剖析IL-6通路如何产生 肿瘤的表型变异性，这驱动了肿瘤的进展并导致对靶向治疗的耐药性[7- 10]。 13]。基于我们的模型，我们将确定和测试治疗这些癌症的最佳治疗方案。

During tumorigenesis, cancer cells as well as healthy cells depend upon growth factors produced in an autocrine and paracrine manner to maintain their proliferafion and differenfiafion. Hence there exists a "tug-ofwar" for growth factors between a tumor and the surrounding non-cancer cells, and the fitness of each individual cell must be defined by this competifion at the populafion level. Importantly, cancer cells on the leading edge of tumors are characterized as being chemo-resistant, have enhanced metastatic potential and are extremely efficient at forming new tumors [6]. We have determined that cancer cells and adjacent stromal cells express high levels of the growth factor IL-6 and activated Stat3 which are hypothesized to be the principal mediators of both tumorigenesis and metastafic progression. In this proposal, we focus on the IL-6/pStat3 pathway in melanoma and breast cancer cells, whose proliferation and differenfiafion crifically depends on pStat3 signals. In the context of understanding tumor dynamics, especially during drug treatment, the intraclonal competifion for growth factors within a genefically-idenfical populafion of cells will be investigated. An understanding of the resulfing phenotypic diversity of tumor cells will lead to improved therapeufic intervenfions eradicafing tumor populafions. Specifically, we will (1) characterize the variable response of tumor cells to growth factors and targeted inhibitors: (2) design a mathemafical framework to predict the consequences of cellular diversity and identify the opfimum therapeutic intervenfion that maximizes the chance of eradicafing the tumor; and (3) validate the predictions of the mathemafical framework in cell lines and murine models. This project fully leverages our expertise in cancer biology and clinical experience (Bromberg), single cell profiling and biochemical modeling (Altan-Bonnet), and mathemafical modeling (Michor). We will dissect how the IL-6 pathway can generate phenotypic variability in tumors, which drives their progression and causes resistance to targeted therapies [7- 13]. Based on our models, we will identify and test the opfimal therapeufic protocol for treafing these cancers.