Dissecting drug resistance in serial uveal melanoma biopsies using integrated, multi-modal single-cell profiling and novel machine learning tools.

使用集成的多模式单细胞分析和新颖的机器学习工具剖析连续葡萄膜黑色素瘤活检中的耐药性。

• 批准号: 10290692
• 负责人: Benjamin Izar
• 金额: $ 18.93万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-08 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10290692
• 关键词: Affect; Biopsy; CRISPR screen; Caring; Cell Line; Cell Nucleus; Cell physiology; Cells; Clinical; Clinical Trials; Cues; Cutaneous; DNA Damage; Data; Data Analyses; Data Set; Dependence; Development; Disease; Drug Targeting; Drug resistance; Ecosystem; Evaluation; Evolution; Exhibits; Freezing; GNAQ gene; Genes; Genomics; Genotype; Goals; Human; Immune checkpoint inhibitor; Impairment; Incidence; Liver; Loss of Heterozygosity; MAP Kinase Gene; MAPK Signaling Pathway Pathway; MEKs; Machine Learning; Maintenance; Melanoma Cell; Metastatic Melanoma; Methods; Modeling; Molecular; Mutate; Mutation; Nature; Neoplasm Metastasis; Operative Surgical Procedures; Pathway interactions; Patients; Pharmacogenomics; Phenotype; Pilot Projects; Protein Subunits; Proteins; RNA; RNA Splicing; Radiation therapy; Resistance; Small Nuclear RNA; Solid Neoplasm; Specimen; Systemic Therapy; Therapeutic; Tissues; Translations; United States; United States Food and Drug Administration; Uveal Melanoma; analytical method; base; brca gene; cancer cell; chemotherapy; combinatorial; disorder control; experience; genome editing; genome-wide; improved; in vitro activity; in vivo Model; inhibitor/antagonist; innovation; loss of function; malignant breast neoplasm; melanoma; molecular subtypes; multimodality; mutational status; novel; preclinical study; prospective; resistance mechanism; response; single cell analysis; therapy resistant; tool; transcriptome; transcriptome sequencing; tumor

PROJECT SUMMARY Uveal melanoma (UM) is a rare melanoma subtype with an estimated annual incidence of approximately 2000 in the United States. While most patients have excellent rates of local disease control with surgery or radiotherapy, nearly half develop metastatic disease, most frequently to the liver. Metastatic UM (MUM) is very treatment resistant and shows no significant responses to conventional chemotherapies or immune checkpoint inhibitors (ICI). UM is molecularly characterized by canonical mutations of the Gα protein subunits (GNAQ/11), which result in hyperactivation of the MAPK pathway. Targeting this pathway with MEK inhibitors (MEKi) results in significant anti-tumor activity in vitro, and response rates of up to 14% in patients with MUM, thereby exhibiting significantly higher activity compared to other available systemic therapies. However, there remains significant potential to improve the efficacy of MEKi. To better define modifiers of MEKi sensitivity and resistance, it is important to consider the fact that most UM harbor mutually exclusive GNAQ/11co-mutations, including inactivating mutations or bi-allelic loss of BAP1 (~33%) or deleterious mutations in SF3B1 (~23%) or EIF1AX (13%), thus define distinct genomic subtypes of UM. These alterations likely provide dependencies that are not abrogated with MEKi alone, yet they may represent synthetic lethal vulnerabilities in the context of MEKi. Furthermore, there has not been a systematic evaluation of how MEKi (or any other therapy) alters cancer cell autonomous and cell non-autonomous mechanisms that could confer drug resistance. This is in part due to technical barriers and lack of in vivo models that faithfully recapitulate human MUM. In this proposal, we build on several innovations to systematically determine the impact of MEKi on the MUM ecosystem and define synthetic lethal dependencies across the UM genomic landscape and in the context of MEKi. We will achieve this in two specifim aims: In Aim 1, we will perform single-nuclei RNA-sequencing (snRNA-seq) in patients with MUM who underwent therapy with MEKi selumetinib and had serial biopsies (pre-, on- and off-therapy), and analyze these with several established analytical methods. Second, building on recent developments, we will build machine learning tools for the analysis of sequential single-cell data sets. In Aim 2, we will perform patient- informed CRISPR-screens with multi-modal single-cell RNA/protein readouts across the genomic spectrum of UM. Finally, we will perform genome-scale CRISPR-screens across multiple models to define genotype-shared and -unique modifiers of MEKi responses. Together, these approaches will provide the a comprehensive sequential single-cell analysis in solid tumors, develop tools for temporal single-cell analyses that can be referenced against a ground truth, and define genotype-dependent synthetic lethal vulnerabilities with concurrent MEKi therapy.

项目概要 葡萄膜黑色素瘤 (UM) 是一种罕见的黑色素瘤亚型，估计年发病率约为 2000 在美国，大多数患者通过手术或其他方法获得了良好的局部疾病控制率。 放射治疗后，近一半会出现转移性疾病，最常见的是转移性 UM (MUM)。 治疗耐药，对传统化疗或免疫检查点没有显着反应 抑制剂 (ICI) 的分子特征是 Gα 蛋白亚基 (GNAQ/11) 的典型突变， 使用 MEK 抑制剂 (MEKi) 靶向该通路会导致 MAPK 通路过度激活。 在体外具有显着的抗肿瘤活性，MUM 患者的缓解率高达 14%，从而表现出 与其他显着可用的全身疗法相比，活性更高，但仍然存在显着的差异。 为了更好地定义 MEKi 敏感性和耐药性的修饰剂，它是提高 MEKi 功效的潜力。 重要的是要考虑到大多数 UM 都存在相互排斥的 GNAQ/11co 突变，包括 BAP1 的失活突变或双等位基因丢失 (~33%) 或 SF3B1 (~23%) 或 EIF1AX 的有害突变 (13%)，因此定义了 UM 的不同基因组亚型，这些改变可能提供了并非如此的依赖关系。 单独使用 MEKi 就可以废除，但它们可能代表 MEKi 背景下的合成致命漏洞。 此外，还没有对 MEKi（或任何其他疗法）如何改变癌细胞进行系统评估 自主和细胞非自主机制可能会产生耐药性。 技术和缺乏忠实地再现人类障碍的体内模型。 多项创新系统地确定 MEKi 对 MUM 生态系统的影响并定义 我们将在 UM 基因组景观和 MEKi 背景下实现合成致死依赖性。 这有两个具体目标：在目标 1 中，我们将对患有以下疾病的患者进行单核 RNA 测序 (snRNA-seq)： 接受 MEKi 司美替尼治疗并进行连续活检（治疗前、治疗中和治疗后）的 MUM，以及 其次，基于最近的发展，我们将使用几种既定的分析方法来分析这些内容。 构建用于分析连续单细胞数据集的机器学习工具在目标 2 中，我们将执行患者- 通过跨基因组谱的多模式单细胞 RNA/蛋白质读数进行知情的 CRISPR 筛选 最后，我们将在多个模型中进行基因组规模的 CRISPR 筛选，以定义基因型共享 和 -MEKi 响应的独特修饰符，这些方法将共同提供全面的。 实体瘤中的连续单细胞分析，开发可用于时间单细胞分析的工具 参考基本事实，并定义基因型依赖的合成致命漏洞 MEKi 疗法。