Computational analysis of tumor ecosystems and their regulation and association with outcomes

项目摘要 肿瘤的细胞组成可以从根本上影响对治疗的反应， 生存结果。来源于肿瘤活检的生物标志物在肿瘤诊断方面取得了一定的成功。 其用于预后或指导治疗决策的临床效用，被以下因素混淆 此外，不同的生物标志物可能是 女性vs男性患者需要。在之前的工作中，我们展示了如何对大型 具有临床注释的临床注释的公共癌症数据集可以鲁棒地识别 与泛癌和非泛癌患者生存相关的特定基因和过程 癌症特有的方式。在这里，我们仍然系统地研究癌症特异性预后 通过将单细胞RNA-seq（scRNAseq）与批量RNA-seq整合， 甲基化数据。我们将在组织微阵列中验证选定的发现。 首先，我们将确定癌症特异性细胞转录状态和生态系统 与生存和治疗反应相关，扩展了先前的工作，确定了10 不同的“生态型”的共同发生的细胞状态跨癌。第二，我们将扩大 我们的框架，以分离癌症特异性细胞类型特异性甲基化谱和他们的 使用配对批量RNA-seq与人群间插补基因表达的相关性 和来自TCGA的甲基化。第三，我们将验证癌症的生存相关性- 通过对人体组织微阵列进行染色来检测特定细胞状态。我们将专注于高等级 浆液性卵巢癌（HGSOC）预后差，非小细胞肺癌（NSCLC） 癌症（NSCLC），我们有关于免疫治疗反应的广泛信息。 我们将在大组织切片上使用CODEX成像来评估空间组织 NSCLC和HGSOC中的结果相关细胞状态。总体而言，我们将全面 绘制癌症特异性细胞状态和恶性肿瘤的生态类型， 生物标志物和可能的新治疗靶点。

Project Summary The cellular makeup of tumors can radically influence response to treatment, and survival outcomes. Biomarkers derived from tumor biopsies have had modest success in their clinical utility for prognosis or guiding treatment decisions, being confounded by factors such as cellular composition of tissues Moreover, different biomarkers may be needed in female vs male patients. In prior work we showed how meta-analysis of large clinically annotated public cancer datasets with clinical annotations can robustly identify specific genes and processes associated with survival for patients in both pan-cancer and cancer-specific ways. Here we still systematically investigate cancer-specific prognostic cell types through integration of single cell RNA-seq (scRNAseq) with bulk RNA-seq and methylation data. We will validate selected findings in tissue microarrays. First, we will identify cancer-specific cell transcriptional states and ecosystems associated with survival and treatment response, extending prior work that identified 10 different “ecotypes” of co-occurring cell states across carcinomas. Second, we will extend our framework to isolate cancer-specific cell-type-specific methylation profiles and their correlation with imputed gene expression across populations using paired bulk RNA-seq and methylation from TCGA. Third, we will validate survival associations of cancer- specific cell states by staining human tissue microarrays. We will focus on high grade serous ovarian cancer (HGSOC), which has dire prognosis, and non small-cell lung cancer (NSCLC) for which we have extensive information on immunotherapy response. We will use CODEX imaging on large tissue sections to assess the spatial organization of outcome-related cell states in NSCLC and HGSOC. Overall, we will comprehensively map cancer-specific cell states and ecotypes across malignancies, identifying potential biomarkers and possible new therapeutic targets.