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

项目总结 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获得了良好的局部疾病控制率 放射治疗中，近一半人发展为转移性疾病，最常见的是肝脏。转移性UM(UM)非常 耐药，对常规化疗或免疫检查点无明显反应 抑制剂(ICI)。UM的分子特征是Gα蛋白亚基(GNAQ/11)的典型突变， 这会导致MAPK通路的过度激活。用MEK抑制剂(Meki)靶向这一途径的结果 在体外具有显著的抗肿瘤活性，在MUM患者中的应答率高达14%，从而展示了 与其他可用的系统疗法相比，活跃度明显更高。然而，仍然有重要的 有可能提高Meki的疗效。为了更好地定义Meki敏感性和抵抗力的修饰物，它是 重要的是要考虑到这样一个事实，即大多数UM都存在相互排斥的GNAQ/11共突变，包括 BAP1的失活突变或双等位基因丢失(~33%)或SF3B1或EIF1AX的有害突变(~23%) (13%)，从而定义了UM的不同基因组亚型。这些更改可能提供的依赖项不是 仅与Meki一起废除，但在Meki的背景下，它们可能代表着合成致命漏洞。 此外，还没有关于Meki(或任何其他疗法)如何改变癌细胞的系统评估。 可能产生抗药性的自主和细胞非自主机制。这部分归因于 技术障碍和缺乏忠实地概括人类妈妈的体内模型。在这个提案中，我们构建 关于系统确定Meki对UM生态系统的影响并定义 UM基因组图景和Meki背景下的合成致死依赖。我们将实现 这有两个特定的目标：在目标1中，我们将在慢性粒细胞白血病患者中进行单核rna测序(snrna-seq)。 接受Meki selumetinib治疗并接受一系列活组织检查(治疗前、治疗中和治疗后)的母亲，以及 用几种既定的分析方法来分析这些问题。第二，在近期事态发展的基础上，我们将 构建机器学习工具，用于分析连续的单细胞数据集。在目标2中，我们将表演病人- 多模式单细胞RNA/蛋白质读数的CRISPR-Screen 恩。最后，我们将在多个模型上进行基因组规模的CRISPR筛选，以定义基因共享 和-Meki反应的独特修饰语。这些方法结合在一起，将提供一个全面的 实体肿瘤的顺序单细胞分析，开发用于临时单细胞分析的工具，可以 以基本事实为参照，并定义与基因相关的合成致命漏洞 梅基疗法。

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.