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，当时的年鉴入射约为2000年 在美国，大多数患者在手术或 放射疗法，将近一半患有转移性疾病，最常见于肝脏。 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）。 这导致MAPK途径过度激活。 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，活动明显更高。 提高MEKI的效力。 重要的是要考虑以下事实：大多数UM港口共同的独家GNAQ/11CO-包括 SF3B1（〜23％）或EIF1AX中的BAP1的灭活或双行性损失（〜33％）或有害突变 （13％），因此定义了UM的不同基因组亚型。 仅凭Meki就消除了，但它们可能在Meki的背景下压制合成的Leticil脆弱性。 此外，对MEKI（或任何其他疗法）的系统评估没有改变癌细胞 可以赋予耐药性的自主和非自主机制。 技术障碍和缺乏体内模型，这些模型忠实地重组了人类妈妈。 关于系统的严重性创新，确定Meki对妈妈生态系统的影响并定义 跨基因组景观和MEKI的合成致命依赖性。 这在两个特定目标中：在AIM 1中，我们将在患有患者的患者中执行核核RNA测序（SNRNA-SEQ） 妈妈接受了Meki selumetinib治疗，并进行了连环活检（前，外疗），并且 用几种建立的分析方法分析这些方法。 构建用于分析AIM 2的连续单细胞数据集的机器学习工具。 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。 和 - 唯一的MEKI响应，这些方法将提供一种表述 实体瘤中的顺序单细胞分析，开发了可以是暂时单细胞分析的工具 引用了一个地面真理，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