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

项目摘要 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 在美国虽然大多数患者通过手术或手术治疗局部疾病控制率很高， 在接受放疗的患者中，近一半发展为转移性疾病，最常见的是转移到肝脏。转移性UM（MUM）非常 治疗抗性，对常规化疗或免疫检查点无显著反应 抑制剂（ICI）。UM的分子特征是Gα蛋白亚基（GNAQ/11）的典型突变， 这导致MAPK通路的过度活化。用MEK抑制剂（MEKi）靶向这一途径导致 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 与其他可用的全身治疗相比，活性显著更高。然而，仍有大量 提高MEKi疗效的潜力。为了更好地定义MEKi灵敏度和电阻的修饰剂， 重要的是要考虑到大多数UM具有相互排斥的GNAQ/11共突变的事实，包括 BAP 1失活突变或双等位基因丢失（约33%）或SF 3B 1或EIF 1AX中的有害突变（约23%） (13%），从而定义UM的不同基因组亚型。这些更改可能提供不需要的依赖项， 虽然它们已经被MEKi单独废除，但它们可能代表MEKi背景下的合成致命弱点。 此外，还没有对MEKi（或任何其他疗法）如何改变癌细胞增殖的系统评价。 自主和细胞非自主机制，可能赋予耐药性。这部分是由于 技术障碍和缺乏忠实地再现人类MUM的体内模型。在这份提案中，我们建立了 关于几项创新，以系统地确定MEKi对MUM生态系统的影响，并定义 在整个UM基因组景观和MEKi的背景下的合成致死依赖性。我们将实现 这有两个具体的目标：在目标1中，我们将在患有以下疾病的患者中进行单核RNA测序（snRNA-seq）： 接受MEKi司美替尼治疗并进行系列活检（治疗前、治疗期间和治疗结束时）的MUM，以及 用几种已建立的分析方法分析这些。第二，根据最近的事态发展，我们将 构建机器学习工具，用于分析连续单细胞数据集。在目标2中，我们将执行患者- CRISPR-屏幕与多模态单细胞RNA/蛋白质读数在整个基因组谱 嗯.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反应的独特修饰剂。总之，这些方法将提供全面的 在实体瘤中的顺序单细胞分析，开发用于时间单细胞分析的工具， 参考地面实况，并定义基因型依赖的合成致命漏洞， MEKi疗法。

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.