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

项目 1：BRAF 突变黑色素瘤适应性耐药的多尺度建模

基本信息

批准号：
10343839
负责人：
PETER Karl SORGER
金额：
$ 53.52万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-08 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10343839
关键词：
Adaptor Signaling Protein Affect Antitumor Response Autocrine Communication BRAF gene Biochemical Biological Assay Biology Cell Line Cell Proliferation Cell Shape Cells Clinical Clustered Regularly Interspaced Short Palindromic Repeats Coculture Techniques Colorectal Cancer Comparative Study Computer Models Consequentialism Cultured Cells Data Differential Equation Drug Exposure Drug resistance Drug usage Endocrine Epigenetic Process Evolution Exposure to FRAP1 gene Feedback Fibroblasts Generations Goals Heritability Homeostasis Hour Image Immune Individual JNK-activating protein kinase Ligands MAP Kinase Gene MAP Kinase Modules MAP3K1 gene MEK inhibition MEKs Malignant Neoplasms Malignant neoplasm of thyroid Masks Measures Mediating Melanoma Cell Methodology Methods Mitogen-Activated Protein Kinase Inhibitor Modeling Mutate Mutation Neural Crest Oncogenic Outcomes Research PTK2 gene Papillary Paracrine Communication Pathway interactions Patients Peptide Hydrolases Pharmaceutical Preparations Pharmacology Phenotype Phosphoric Monoester Hydrolases Phosphotransferases Play Process Property Proteomics Proto-Oncogene Proteins B-raf Proto-Oncogene Proteins c-akt Publishing Receptor Protein-Tyrosine Kinases Regulation Residual Tumors Resistance Role Signal Pathway Signal Transduction System Testing Therapeutic Time acquired drug resistance anticancer activity autocrine base cancer cell cell behavior cell type cellular imaging comparative drug-sensitive erbB-2 Receptor immune checkpoint inhibitor kinase inhibitor kinetic model melanocyte melanoma multi-scale modeling multiple omics mutant neoplastic cell operation phenomenological models primitive cell receptor receptor expression receptor upregulation resistance mechanism resistance mutation response single-cell RNA sequencing small molecule statistical and machine learning thyroid neoplasm time use tool trafficking tumor

项目摘要

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.

项目摘要 – 项目 1（目标 4）：BRAF 适应性耐药性的多尺度建模项目 1 的总体目标是对突变黑色素瘤进行综合、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及比较研究在 BRAF 突变型甲状腺癌和结直肠癌中进行的研究是理解的主要挑战之一。适应性耐药性是所提出的机制的绝对多样性，范围从 MAPK 的重新激活信号传导，参与平行的 PI3K/mTOR/AKT 信号级联和改变的受体运输。个别发表的研究重点关注这些现象的子集，通常是在不同的细胞系中，并且它仍然不清楚重点的差异是否反映了基础生物学、方法学（单细胞）的差异例如 RNASeq 与蛋白质组学）或时间尺度（小时与周）。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，然而，受体运输和并行信号级联都发挥作用，因为单个患者就可以。每个黑色素瘤都具有不同的耐药突变组，观察到的多样性可能是另一个极端是，每种肿瘤都会找到一种独特的方式来产生耐药性，而我们。我们相信，最可能的解释是介于两者之间。这些极端情况：适应涉及一些生化上不同的机制，这些机制可能有多种呈现方式取决于细胞类型、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抵抗来检验这一假设在一些 BRAF 突变细胞系中，结合在更广泛的细胞类型中进行更多现象学建模。目标 4.1 将使用单细胞数据和 ODE 网络来研究即早期 BRAF/MEK/ERK 的稳态 (MAPK) 信号在四种细胞系中阐明负反馈环路所起的作用，涉及目标 4.2 将检查去分化现象和目标 4.3 将使用可能导致残留疾病的缓慢循环的药物不敏感细胞的产生。类似的深入方法来研究 ADAM 蛋白酶活性的变化和受体脱落，从而导致持续的自分泌和旁分泌信号传导以及 MAPK 活性的增加将着眼于时间。适应的进化基于初步证据表明，在单个细胞系中，适应可以涉及短期（1-2 天）的 MAPK 反馈以及受体生物学的去分化和变化长期（几天到几周）目标 4.5 将在一组 20 个 BRAF 突变细胞中使用多组学分析。线以确定目标 4.1 至 4.4 中分析的机制的变异程度。学习方法将识别细胞内和自分泌/内分泌信号的变化表型的结果。