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

肿瘤生态系统及其调节及其与结果关联的计算分析

• 批准号: 10568399
• 负责人: Andrew J. Gentles
• 金额: $ 62.28万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10568399
• 关键词: Architecture; Atlas of Cancer Mortality in the United States; Awareness; Biological Markers; Biopsy; Cancer Patient; Carcinoma; Cells; Clinical; Computer Analysis; Custom; Data; Data Set; Disease Resistance; Ecosystem; Environment; Exclusion; Female; Future; Gene Expression; Genes; Genetic Transcription; Image; Immune; Immunotherapy; Infiltration; Inflammatory; Knowledge; Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Malignant neoplasm of urinary bladder; Maps; Meta-Analysis; Methods; Methylation; Modeling; Non-Small-Cell Lung Carcinoma; Outcome; Patients; Phenotype; Population; Process; Prognosis; Regulation; Relapse; Research Personnel; Resistance; Sampling; Serous; Sex Differences; Signal Transduction; Solid Carcinoma; Stains; T-Lymphocyte; Testing; The Cancer Genome Atlas; Tissue Microarray; Tissues; Treatment outcome; Work; biomarker identification; cancer type; cell behavior; cell type; cellular imaging; cohort; computer framework; cytotoxic; exhaust; experience; human tissue; in vivo evaluation; innovation; male; melanoma; new therapeutic target; potential biomarker; prognostic; programs; research clinical testing; response; sex; single-cell RNA sequencing; success; survival outcome; targeted cancer therapy; therapeutic target; transcriptome sequencing; treatment response; tumor

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.

项目摘要 肿瘤的细胞构成可以从根本上影响对治疗的反应，并且 生存结果。源自肿瘤活检的生物标志物在 他们用于预后或指导治疗决策的临床实用性 此外，组织的细胞组成等因素，可能是不同的生物标志物 在女性与男性患者中需要。在先前的工作中，我们展示了大型的荟萃分析 具有临床注释的临床注释的公共癌数据集可以牢固地识别 与患者的生存相关的特定基因和过程，泛滥和患者的生存率 特定于癌症的方式。在这里，我们仍系统地研究癌症特异性的预后 通过将单细胞RNA-seq（Scrnaseq）与散装RNA-Seq和 甲基化数据。我们将验证组织微阵列中选定的发现。 首先，我们将确定癌症特异性的细胞转录状态和生态系统 与生存和治疗反应相关，扩展了确定的先前工作10 跨癌的同时存在的细胞态的不同“生态型”。其次，我们将扩展 我们分离癌症特异性细胞型特异性甲基化谱及其框架 使用配对的大量RNA-Seq在种群中与估算的基因表达相关 和TCGA的甲基化。第三，我们将验证癌症的生存关联 特定的细胞态通过染色人体组织微阵列。我们将专注于高级 具有可怕预后的浆液卵巢癌（HGSOC）和非小细胞肺 癌症（NSCLC）我们为免疫疗法反应提供了广泛的信息。 我们将在大型组织部分上使用法典成像来评估空间组织 NSCLC和HGSOC中与结果相关的细胞态的。总体而言，我们将全面 跨恶性肿瘤的地图癌症特异性细胞状态和生态型，确定潜力 生物标志物和可能的新治疗靶标。