Project 1: Multi-scale modeling of adaptive drug resistance in BRAF-mutant melanoma

项目总结-项目1（AIM 4）：BRAF适应性耐药的多尺度建模- 突变型黑素瘤项目1的总体目标是对以下方面有一个综合的、定量的了解： 黑色素瘤对靶向BRAF和MEK激酶抑制剂的适应性耐药性，以及比较研究 在BRAF突变型甲状腺癌和结直肠癌中进行。理解的主要挑战之一 适应性耐药是提出的机制的绝对多样性，从MAPK的重新激活， 信号传导，参与平行的PI 3 K/mTOR/AKT信号级联和改变的受体运输。 个别发表的研究集中在这些现象的子集，通常在不同的细胞系，它仍然是 不清楚重点的差异是否反映了基础生物学、方法学（单细胞） 例如，RNASeq v.蛋白质组学）或时间尺度（小时v.周）。一种可能是 现象学的多样性掩盖了一个共同机制的运作，其中反馈途径， 受体运输和平行信号级联都发挥作用。因为一个病人可以 每个黑色素瘤都有不同的耐药突变，观察到的多样性可能是 有意义的另一个极端是，每个肿瘤都有一种独特的方式产生抗药性， 会发现很少的基本原理。我们认为最有可能的解释是介于 这些极端：适应涉及少数生化不同的机制，可以有各种各样的 呈现取决于细胞类型、微环境、测定方法和时间尺度。 我们将通过详细的动力学模型和单细胞数据研究适应性抗性来验证这一假设 在一些BRAF突变细胞系中，结合更广泛的细胞类型中的更多现象学建模。 目的4.1将使用单细胞数据和ODE网络研究即刻早期BRAF/MEK/ERK的稳态 （MAPK）信号转导，以阐明负反馈回路所发挥的作用， 磷酸酶和衔接蛋白。目标4.2将研究去分化现象和 产生缓慢循环的药物不敏感细胞，可能导致残留疾病。Aim 4.3将使用 类似的深入方法来研究ADAM蛋白酶活性和受体脱落的变化， 持续的自分泌和旁分泌信号和增加的MAPK活性。Aim 4.4将查看时间 初步证据表明，在单个细胞系中，适应性可以 涉及MAPK反馈在短期内（1-2天）和去分化和受体生物学上的变化， 更长的时间（几天到几周）。Aim 4.5将在一组20个BRAF突变细胞中使用多组学分析 确定目标4.1至4.4中分析的机制的变异程度。统计和机器 学习方法将识别细胞内和自分泌/内分泌信号的变化， 对表型的影响。

PROJECT SUMMARY – PROJECT 1 (AIM 4): Multi-scale modeling of adaptive drug resistance in BRAF- mutant melanoma. The overall goal of Project 1 is to develop an integrated, quantitative understanding of adaptive drug resistance to targeted BRAF and MEK kinase inhibitors in melanoma, with comparative studies performed in BRAF mutant thyroid and colorectal cancers. One of the primary challenges in understanding adaptive drug resistance is the sheer diversity of proposed mechanisms, ranging from reactivation of MAPK signaling, to engagement of parallel PI3K/mTOR/AKT signaling cascades and altered receptor trafficking. Individual published studies focus on subsets of these phenomena, often in different cell lines, and it remains unclear whether differences in emphasis reflect differences in the underlying biology, methodology (single cell RNASeq v. proteomics for example) or time scale (hours vs. weeks). One possibility is that the phenomenological diversity masks the operation of a common mechanism, in which feedback pathways, receptor trafficking, and parallel signaling cascades all play a role. However, because a single patient can harbor melanomas each with a different set of resistance mutations, the observed diversity is likely to be meaningful. The other extreme is that every tumor finds a unique way to become drug resistant, and that we will discover few if any underlying principles. We believe that the most likely explanation lies midway between these extremes: adaptation involves a handful of biochemically distinct mechanisms that can have a variety of presentations depending on cell type, microenvironment, assay method and time scale. We will test this hypothesis by studying adaptive resistance with detailed kinetic modeling and single cell data in a few BRAF-mutant cell lines combined with more phenomenological modeling in a wider range of cell types. Aim 4.1 will use single-cell data and ODE networks to study homeostasis in immediate-early BRAF/MEK/ERK (MAPK) signaling in four cell lines to elucidate the role played by negative feedback loops involving phosphatases and adaptor proteins. Aim 4.2 will examine the phenomenon of de-differentiation and the generation of slowly cycling drug-insensitive cells likely to contribute to residual disease. Aim 4.3 will use similar in-depth methods to study changes in ADAM protease activity and receptor shedding that cause sustained autocrine and paracrine signaling and increased MAPK activity. Aim 4.4 will look at the time evolution of adaptation based on preliminary evidence showing that, in a single cell line, adaptations can involve MAPK feedback in the short term (1-2 days) and de-differentiation and changes in receptor biology on a longer term (days to weeks). Aim 4.5 will use multi-omic analysis across a panel of 20 BRAF mutant cells lines to establish the extent of variability in mechanisms analyzed in Aims 4.1 to 4.4. Statistical and machine learning approaches will identify the changes in intracellular and autocrine/endocrine signaling most consequential for phenotype.