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

项目摘要--项目1(目标4)：对BRAF适应性耐药性进行多尺度建模-- 突变黑色素瘤。项目1的总体目标是开发一个完整的、量化的理解 黑色素瘤患者对靶向BRAF和MEK激酶抑制剂的适应性耐药性及其比较研究 在BRAF突变的甲状腺癌和结直肠癌中进行。理解以下问题的主要挑战之一 适应性耐药是已提出的机制的绝对多样性，从MAPK的重新激活 信号，与并行的PI3K/mTOR/AKT信号级联和改变的受体交易有关。 个别发表的研究集中在这些现象的亚群上，通常是在不同的细胞系中，而且它仍然存在 不清楚侧重点的差异是否反映了潜在的生物学、方法学(单细胞)的差异 例如，RNAseq诉蛋白质组学)或时间尺度(小时与周)。一种可能性是， 现象学多样性掩盖了共同机制的运行，在这种机制中，反馈路径， 受体交易和平行的信号级联都起到了作用。然而，因为一个病人可以 海港黑色素瘤每个都有一套不同的耐药突变，观察到的多样性可能是 很有意义。另一个极端是，每个肿瘤都会找到一种独特的抗药性方式，而我们 几乎不会发现任何潜在的原则。我们认为，最有可能的解释是介于 这些极端：适应涉及几个生化上不同的机制，这些机制可以有各种 陈述取决于细胞类型、微环境、分析方法和时间尺度。 我们将通过用详细的动力学模型和单细胞数据研究适应性阻力来检验这一假设 在一些BRAF突变的细胞系中，结合了更多的现象学建模，在更广泛的细胞类型中。 AIM 4.1将使用单细胞数据和ODE网络来研究即刻早期BRAF/MEK/ERK的动态平衡 (MAPK)在四种细胞系中的信号转导，以阐明负反馈环所起的作用 磷酸酶和接头蛋白。Aim 4.2将研究去分化现象和 缓慢循环的药物不敏感细胞的产生可能会导致残留疾病。AIM 4.3将使用 用类似的深入方法研究导致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将关注时间 基于初步证据的适应进化，初步证据表明，在单个细胞系中，适应可以 在短期内(1-2天)参与MAPK反馈和受体生物学的去分化和变化 更长的时间(几天到几周)。Aim 4.5将对20个BRAF突变细胞进行多染色体分析 确定目标4.1至4.4中分析的机制的可变性程度。统计与机器 学习方法将最多地识别细胞内和自分泌/内分泌信号的变化 对表型有影响。

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.