Spatially resolved single-cell patterns of drug-resistant ovarian cancers

耐药卵巢癌的空间分辨单细胞模式

基本信息

批准号：
9922656
负责人：
Benjamin Robert King
金额：
$ 6.53万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9922656
关键词：
3-Dimensional Affect Aftercare Automobile Driving Blood Vessels Cancer Patient Candidate Disease Gene Cell Culture Techniques Cell Line Cells Cellular Morphology Cellular Structures Clustered Regularly Interspaced Short Palindromic Repeats Combined Modality Therapy Common Neoplasm Complex Cues Data Data Set Development Disease Progression Drug resistance Evolution Future Gene Expression Profile Generations Genes Genetic Genetic Transcription Image Imaging Techniques In Vitro Individual Knowledge Ligands Light Malignant Female Reproductive System Neoplasm Malignant Neoplasms Malignant neoplasm of ovary Maps Messenger RNA Methods Microscopy Modeling Molecular Molecular Genetics Molecular Target Neoplasm Metastasis Organoids Patients Pattern Phenotype Population Positioning Attribute Probability Process Reaction Recurrence Regimen Relapse Research Resistance Sampling Serous Signal Pathway Signal Transduction Signaling Molecule Small Interfering RNA Social Interaction Solvents Spatial Distribution Supporting Cell Thick Three-Dimensional Image Time Tissues Tumor Tissue Tumor Volume Validation Woman Work Xenograft Model Xenograft procedure base cancer cell cancer type cell type genetic profiling genetic signature genome-wide improved in vivo in vivo Model inhibitor/antagonist insight knock-down knockout gene mRNA Expression mortality neoplastic cell next generation novel ovarian neoplasm prevent profiles in patients programs receptor resistance gene single molecule single-cell RNA sequencing small molecule survival outcome targeted imaging therapeutic target transcriptome tumor tumor microenvironment

项目摘要

Project Summary Ovarian cancers remain one of the deadliest cancers affecting women. Tumors commonly acquire resistance to first-line chemotherapeutics, and only 30% of patients survive beyond 5 years. Novel targeted combination therapies are needed to improve long-term survival outcomes and will depend on an improved understanding of the molecular and genetic mechanisms of drug resistance. Previous work has used next-generation bulk sequencing approaches to globally profile genetic signatures of drug resistance in ovarian cancer tissue. However, rare cells in a highly heterogeneous 3D tissue context may hold the key to understanding these complex processes, and such rare cells are notoriously difficult to identify using these standard methods. We will use an in vivo patient derived xenograft (PDX) model of high grade serous ovarian cancer (HSGSOC) representing multiple different genetic backgrounds. HGSOC PDXs will be allowed to acquire resistance to PARP inhibitor talazoparib. Transcriptional signatures of PARP inhibitor resistance will be assessed using single cell RNA sequencing of single cells isolated from tumor tissue fully resistant to inhibitor as well as from tissue collected at intermediate time points. scRNA seq data will be mined to gain transcriptional signatures of (1) component cell populations and (2) candidate genes driving the resistant phenotype. We will use multiplex single molecule FISH and light sheet microscopy to image target (n ~ 100) mRNA species in thick tissue blocks. We will analyze 3D image datasets to identify resistant cancer cells and chart their 3D position in relation to supporting cell types that express relevant cell signaling ligands and/or receptors and additional tumor features (e.g. stroma, blood vessels). Lastly, we will choose target genes that will be functionally validated in relevant cell culture and additional PDX in vivo models. By examining single cell transcriptional profiles, we will greatly advance our understanding of how the 3D tumor tissue microenvironment allows and encourages rare cells with pre-resistant transcriptional programs to escape PARP inhibitor treatment. Along with an improved understanding of the dynamics of drug resistance, the proposed research has the potential to suggest novel combination treatments that could be exploited in the future to more effectively eliminate HGSOC and prevent recurrence of drug resistant tumors.

项目概要卵巢癌仍然是影响女性最致命的癌症之一。肿瘤通常会获得对一线化疗药物产生耐药性，只有 30% 的患者存活超过 5 年。小说需要靶向联合疗法来改善长期生存结果，并且取决于更好地了解耐药性的分子和遗传机制。以前的工作使用下一代批量测序方法来全球分析基因特征卵巢癌组织中的耐药性。然而，高度异质的 3D 组织中的稀有细胞上下文可能是理解这些复杂过程的关键，而这种罕见的细胞是众所周知，使用这些标准方法很难识别。我们将使用源自患者体内的高级别浆液性卵巢癌 (HSGSOC) 的异种移植 (PDX) 模型代表多种不同的遗传背景。 HGSOC PDX 将被允许获得对 PARP 抑制剂他拉佐帕尼 (talazoparib) 的耐药性。 PARP 抑制剂耐药性的转录特征将使用单细胞 RNA 进行评估对从对抑制剂完全耐药的肿瘤组织以及组织中分离的单细胞进行测序在中间时间点收集。 scRNA seq 数据将被挖掘以获得转录特征 (1) 组成细胞群和 (2) 驱动抗性表型的候选基因。我们将使用多重单分子 FISH 和光片显微镜对目标 (n ~ 100) mRNA 物种进行成像厚的组织块。我们将分析 3D 图像数据集来识别耐药癌细胞并绘制它们的图表与表达相关细胞信号配体的支持细胞类型相关的 3D 位置和/或受体和其他肿瘤特征（例如基质、血管）。最后，我们将选择目标基因将在相关细胞培养物和其他 PDX 体内模型中进行功能验证。经过通过检查单细胞转录谱，我们将极大地加深对 3D 肿瘤组织微环境允许并鼓励具有预抗性转录的稀有细胞逃避 PARP 抑制剂治疗的计划。随着对动力学的进一步了解对于耐药性，拟议的研究有可能提出新的联合治疗方法将来可以利用它来更有效地消除 HGSOC 并防止再次发生耐药肿瘤。