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: 10447792
• 负责人: Benjamin Izar
• 金额: $ 22.27万
• 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/10447792
• 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; inhibitor therapy; 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.

项目摘要 紫紫色黑色素瘤（UM）是一种罕见的黑色素瘤亚型，估计年度大约2000年 在美国。大多数患者通过手术或 放射疗法几乎一半患有转移性疾病，最常见于肝脏。转移性UM（妈妈）非常 耐药性，对常规化学疗法或免疫检查点没有明显的反应 抑制剂（ICI）。 UM的分子特征是Gα蛋白亚基的典型突变（GNAQ/11）， 这导致MAPK途径过度激活。用MEK抑制剂（MEKI）靶向这一途径 在体外的显着抗肿瘤活性中，妈妈患者的反应率最高14％，从而表现出 与其他可用的全身疗法相比，活性明显更高。但是，仍然很重要 提高MEKI效率的潜力。为了更好地定义meki敏感性和抗性的修饰符，它是 重要的是要考虑以下事实：大多数UM港口相互排斥的GNAQ/11CO-包括 SF3B1（〜23％）或EIF1AX中的BAP1的灭活突变或BI-平行性损失（〜33％）或有害突变 （13％），因此定义了UM的不同基因组亚型。这些更改可能会提供没有的依赖性 单独使用Meki牵引，但它们可能代表Meki的综合致命脆弱性。 此外，还没有对MEKI（或任何其他疗法）改变癌细胞的系统评估 可以赋予耐药性的自主和细胞非自主机制。这部分是由于 技术障碍和缺乏忠实地概括人类妈妈的体内模型。在此提案中，我们建立 几项创新，以系统地确定Meki对妈妈生态系统的影响并定义 整个UM基因组景观和MEKI的合成依赖性。我们将实现 这在两个特定的目的中：在AIM 1中，我们将对患有的患者进行单核RNA测序（SNRNA-SEQ） 接受Meki selumetinib治疗的妈妈，并进行了连环活检（预先进行治疗），以及 用几种既定的分析方法分析这些方法。其次，基于最近的发展，我们将 构建用于分析顺序单细胞数据集的机器学习工具。在AIM 2中，我们将执行患者 - 带有多模式的单细胞RNA/蛋白质读数的知情CRISPR屏幕，跨基因组光谱 嗯。最后，我们将在多个模型上执行基因组规模的CRISPR屏幕，以定义基因型共享 Meki响应的唯一修饰符。这些方法一起将提供全面的 实体瘤中的顺序单细胞分析，开发用于临时单细胞分析的工具，可以是 引用了地面真理，并与并发定义了依赖基因型的合成致死性脆弱性 meki疗法。

项目成果

Non-cell-autonomous cancer progression from chromosomal instability.

• DOI: 10.1038/s41586-023-06464-z
• 发表时间: 2023-08
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Li, Jun;Hubisz, Melissa J;Earlie, Ethan M;Duran, Mercedes A;Hong, Christy;Varela, Austin A;Lettera, Emanuele;Deyell, Matthew;Tavora, Bernardo;Havel, Jonathan J;Phyu, Su M;Amin, Amit Dipak;Budre, Karolina;Kamiya, Erina;Cavallo, Julie-Ann;Garris, Christopher;Powell, Simon;Reis-Filho, Jorge S;Wen, Hannah;Bettigole, Sarah;Khan, Atif J;Izar, Benjamin;Parkes, Eileen E;Laughney, Ashley M;Bakhoum, Samuel F
• 通讯作者: Bakhoum, Samuel F