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

项目摘要 卵巢癌仍然是影响女性的最致命的癌症之一。肿瘤通常会获得 对一线化疗药物耐药，只有30%的患者存活超过5年。小说 需要有针对性的联合治疗来改善长期生存结果，并将取决于 提高了对耐药的分子和遗传机制的理解。上一首 工作已经使用下一代批量测序方法在全球范围内描绘出 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 背景可能是理解这些复杂过程的关键，而这种罕见的细胞是 使用这些标准方法识别是出了名的困难。我们将使用体内患者派生的 高度恶性浆液性卵巢癌异种移植模型的建立 遗传背景。HGSOC PDX将被允许对PARP抑制剂他唑帕利产生耐药性。 将使用单细胞RNA评估PARP抑制剂耐药性的转录特征 从完全耐药的肿瘤组织和组织中分离的单细胞的测序 在中间时间点收集。将挖掘scRNA序列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)mRNAs 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 与表达相关细胞信号配体和/或的支持细胞类型相关的3D位置 受体和其他肿瘤特征(如间质、血管)。最后，我们将选择目标基因 这将在相关的细胞培养和体内其他PDX模型中进行功能验证。通过 研究单细胞转录图谱，我们将极大地促进我们对3D 肿瘤组织微环境允许和鼓励具有预耐药转录的稀有细胞 逃避PARP抑制剂治疗的程序。随着对动力学的更好的理解 在耐药性方面，拟议的研究有可能提出新的联合治疗方法 将来可以利用这一点来更有效地消除HGSOC并防止再次发生 耐药肿瘤。

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.