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

项目摘要 卵巢癌仍然是影响妇女的最致命的癌症之一。肿瘤通常会获得 对一线化疗药物的耐药性，只有30%的患者存活超过5年。小说 需要靶向联合治疗来改善长期生存结局， 提高对耐药性的分子和遗传机制的理解。先前 工作已经使用下一代批量测序方法来全球分析 卵巢癌组织的耐药性。然而，高度异质的3D组织中的罕见细胞 背景可能是理解这些复杂过程的关键，这些罕见的细胞是 使用这些标准方法很难识别。我们将使用体内患者来源的 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 基因背景将允许HGSOC PDX获得对PARP抑制剂talazoparib的耐药性。 将使用单细胞RNA评估PARP抑制剂抗性的转录特征 从对抑制剂完全耐受的肿瘤组织以及从组织中分离的单细胞的测序 在中间时间点收集。将挖掘scRNA seq数据以获得转录签名 （1）组成细胞群体和（2）驱动抗性表型的候选基因。我们将使用 多重单分子FISH和光片显微镜成像靶（n ~ 100）mRNA种类， 厚厚的组织块我们将分析3D图像数据集，以识别耐药癌细胞，并绘制其 相对于表达相关细胞信号传导配体的支持细胞类型的3D位置和/或 受体和其他肿瘤特征（例如基质、血管）。最后，我们将选择靶基因 其将在相关细胞培养物和其他PDX体内模型中进行功能验证。通过 检查单细胞转录谱，我们将大大推进我们对3D基因是如何表达的理解。 肿瘤组织微环境允许并鼓励罕见的细胞具有前耐药转录 逃避PARP抑制剂治疗的计划。沿着对动态的更好理解 对于耐药性，拟议的研究有可能提出新的联合治疗方法， 这可以在未来加以利用，以更有效地消除HGSOC，并防止再次发生 抗药性肿瘤

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.